1 Introduction

State the marker of interest, the study objectives, and hypotheses (Knijn, Simmer, and Nagtegaal 2015).¹

2 Materials & Methods

Describe Materials and Methods as highlighted in (Knijn, Simmer, and Nagtegaal 2015).²

Describe patient characteristics, and inclusion and exclusion criteria
Describe treatment details
Describe the type of material used
Specify how expression of the biomarker was assessed
Describe the number of independent (blinded) scorers and how they scored
State the method of case selection, study design, origin of the cases, and time frame
Describe the end of the follow-up period and median follow-up time
Define all clinical endpoints examined
Specify all applied statistical methods
Describe how interactions with other clinical/pathological factors were analyzed

2.1 Header Codes

Codes for general settings.³

Setup global chunk settings⁴

knitr::opts_chunk$set(
    eval = TRUE,
    echo = TRUE,
    fig.path = here::here("figs/"),
    message = FALSE,
    warning = FALSE,
    error = FALSE,
    cache = FALSE,
    comment = NA,
    tidy = TRUE,
    fig.width = 6,
    fig.height = 4
)

Load Library

see R/loadLibrary.R for the libraries loaded.

source(file = here::here("R", "loadLibrary.R"))

2.2 Generate Fake Data

Codes for generating fake data.⁵

Generate Fake Data

This code generates a fake histopathological data. Some sources for fake data generation here⁶ , here⁷ , here⁸ , here⁹ , here¹⁰ , here¹¹ , here¹² , here¹³ , and here¹⁴ .

Use this code to generate fake clinicopathologic data

source(file = here::here("R", "gc_fake_data.R"))

wakefield::table_heat(x = fakedata, palette = "Set1", flip = TRUE, print = TRUE)

2.3 Import Data

Codes for importing data.¹⁵

Read the data

library(readxl)
mydata <- readxl::read_excel(here::here("data", "mydata.xlsx"))
# View(mydata) # Use to view data after importing

Add code for import multiple data purrr reduce

2.4 Study Population

2.4.1 Report General Features

Codes for reporting general features.¹⁶

Dataframe Report

# Dataframe report
mydata %>% select(-contains("Date")) %>% report::report(.)

The data contains 250 observations of the following variables:
  - ID: 250 entries: 001, n = 1; 002, n = 1; 003, n = 1 and 247 others
  - Name: 249 entries: Aansh, n = 1; Abdurahmon, n = 1; Abrah, n = 1 and 246 others (1 missing)
  - Sex: 2 entries: Male, n = 126; Female, n = 123 (1 missing)
  - Age: Mean = 50.39, SD = 14.06, range = [25, 73], 1 missing
  - Race: 7 entries: White, n = 162; Hispanic, n = 39; Black, n = 26 and 4 others (1 missing)
  - PreinvasiveComponent: 2 entries: Absent, n = 186; Present, n = 63 (1 missing)
  - LVI: 2 entries: Absent, n = 157; Present, n = 92 (1 missing)
  - PNI: 2 entries: Absent, n = 171; Present, n = 78 (1 missing)
  - Death: 2 levels: FALSE (n = 73); TRUE (n = 176) and missing (n = 1)
  - Group: 2 entries: Control, n = 131; Treatment, n = 118 (1 missing)
  - Grade: 3 entries: 3, n = 101; 1, n = 83; 2, n = 65 (1 missing)
  - TStage: 4 entries: 4, n = 101; 3, n = 66; 2, n = 50 and 1 other (1 missing)
  - Anti-X-intensity: Mean = 2.43, SD = 0.64, range = [1, 3], 1 missing
  - Anti-Y-intensity: Mean = 2.06, SD = 0.78, range = [1, 3], 1 missing
  - LymphNodeMetastasis: 2 entries: Absent, n = 153; Present, n = 96 (1 missing)
  - Valid: 2 levels: FALSE (n = 137); TRUE (n = 112) and missing (n = 1)
  - Smoker: 2 levels: FALSE (n = 125); TRUE (n = 124) and missing (n = 1)
  - Grade_Level: 3 entries: high, n = 100; low, n = 77; moderate, n = 72 (1 missing)
  - DeathTime: 2 entries: Within1Year, n = 149; MoreThan1Year, n = 101

mydata %>% explore::describe_tbl()

250 observations with 21 variables
19 variables containing missings (NA)
0 variables with no variance

2.5 Ethics and IRB

2.5.1 Always Respect Patient Privacy

Always Respect Patient Privacy
- Health Information Privacy¹⁷
- Kişisel Verilerin Korunması¹⁸

2.6 Define Variable Types

Codes for defining variable types.¹⁹

2.6.1 Find Key Columns

print column names as vector

dput(names(mydata))

c("ID", "Name", "Sex", "Age", "Race", "PreinvasiveComponent", 
"LVI", "PNI", "LastFollowUpDate", "Death", "Group", "Grade", 
"TStage", "Anti-X-intensity", "Anti-Y-intensity", "LymphNodeMetastasis", 
"Valid", "Smoker", "Grade_Level", "SurgeryDate", "DeathTime")

2.6.1.1 Find ID and key columns to exclude from analysis

See the code as function in R/find_key.R.

keycolumns <- mydata %>% sapply(., FUN = dataMaid::isKey) %>% as_tibble() %>% select(which(.[1, 
    ] == TRUE)) %>% names()
keycolumns

[1] "ID"   "Name"

2.6.2 Variable Types

Get variable types

mydata %>% select(-keycolumns) %>% inspectdf::inspect_types()

# A tibble: 4 x 4
  type             cnt  pcnt col_name  
  <chr>          <int> <dbl> <list>    
1 character         11  57.9 <chr [11]>
2 logical            3  15.8 <chr [3]> 
3 numeric            3  15.8 <chr [3]> 
4 POSIXct POSIXt     2  10.5 <chr [2]>

mydata %>% select(-keycolumns, -contains("Date")) %>% describer::describe() %>% knitr::kable(format = "markdown")

.column_name	.column_class	.column_type	.count_elements	.mean_value	.sd_value	.q0_value	.q25_value	.q50_value	.q75_value	.q100_value
Sex	character	character	250	NA	NA	Female	NA	NA	NA	Male
Age	numeric	double	250	50.389558	14.0570859	25	38	50	63	73
Race	character	character	250	NA	NA	Asian	NA	NA	NA	White
PreinvasiveComponent	character	character	250	NA	NA	Absent	NA	NA	NA	Present
LVI	character	character	250	NA	NA	Absent	NA	NA	NA	Present
PNI	character	character	250	NA	NA	Absent	NA	NA	NA	Present
Death	logical	logical	250	NA	NA	FALSE	NA	NA	NA	TRUE
Group	character	character	250	NA	NA	Control	NA	NA	NA	Treatment
Grade	character	character	250	NA	NA	1	NA	NA	NA	3
TStage	character	character	250	NA	NA	1	NA	NA	NA	4
Anti-X-intensity	numeric	double	250	2.429719	0.6382312	1	2	3	3	3
Anti-Y-intensity	numeric	double	250	2.060241	0.7779636	1	1	2	3	3
LymphNodeMetastasis	character	character	250	NA	NA	Absent	NA	NA	NA	Present
Valid	logical	logical	250	NA	NA	FALSE	NA	NA	NA	TRUE
Smoker	logical	logical	250	NA	NA	FALSE	NA	NA	NA	TRUE
Grade_Level	character	character	250	NA	NA	high	NA	NA	NA	moderate
DeathTime	character	character	250	NA	NA	MoreThan1Year	NA	NA	NA	Within1Year

Plot variable types

mydata %>% select(-keycolumns) %>% inspectdf::inspect_types() %>% inspectdf::show_plot()

# https://github.com/ropensci/visdat
# http://visdat.njtierney.com/articles/using_visdat.html
# https://cran.r-project.org/web/packages/visdat/index.html
# http://visdat.njtierney.com/

# visdat::vis_guess(mydata)

visdat::vis_dat(mydata)

mydata %>% explore::explore_tbl()

2.6.3 Define Variable Types

2.6.3.1 Find `character` variables

characterVariables <- mydata %>% select(-keycolumns) %>% inspectdf::inspect_types() %>% 
    dplyr::filter(type == "character") %>% dplyr::select(col_name) %>% pull() %>% 
    unlist()

characterVariables

 [1] "Sex"                  "Race"                 "PreinvasiveComponent"
 [4] "LVI"                  "PNI"                  "Group"               
 [7] "Grade"                "TStage"               "LymphNodeMetastasis" 
[10] "Grade_Level"          "DeathTime"

2.6.3.2 Find `categorical` variables

categoricalVariables <- mydata %>% dplyr::select(-keycolumns, -contains("Date")) %>% 
    describer::describe() %>% janitor::clean_names() %>% dplyr::filter(column_type == 
    "factor") %>% dplyr::select(column_name) %>% dplyr::pull()

categoricalVariables

character(0)

2.6.3.3 Find `continious` variables

continiousVariables <- mydata %>% dplyr::select(-keycolumns, -contains("Date")) %>% 
    describer::describe() %>% janitor::clean_names() %>% dplyr::filter(column_type == 
    "numeric" | column_type == "double") %>% dplyr::select(column_name) %>% dplyr::pull()

continiousVariables

[1] "Age"              "Anti-X-intensity" "Anti-Y-intensity"

2.6.3.4 Find `numeric` variables

numericVariables <- mydata %>% select(-keycolumns) %>% inspectdf::inspect_types() %>% 
    dplyr::filter(type == "numeric") %>% dplyr::select(col_name) %>% pull() %>% unlist()

numericVariables

[1] "Age"              "Anti-X-intensity" "Anti-Y-intensity"

2.6.3.5 Find `integer` variables

integerVariables <- mydata %>% select(-keycolumns) %>% inspectdf::inspect_types() %>% 
    dplyr::filter(type == "integer") %>% dplyr::select(col_name) %>% pull() %>% unlist()

integerVariables

NULL

2.6.3.6 Find `list` variables

listVariables <- mydata %>% select(-keycolumns) %>% inspectdf::inspect_types() %>% 
    dplyr::filter(type == "list") %>% dplyr::select(col_name) %>% pull() %>% unlist()
listVariables

NULL

2.6.3.7 Find `date` variables

is_date <- function(x) inherits(x, c("POSIXct", "POSIXt"))

dateVariables <- names(which(sapply(mydata, FUN = is_date) == TRUE))
dateVariables

[1] "LastFollowUpDate" "SurgeryDate"

2.7 Overview the Data

Codes for overviewing the data.²⁰

2.7.1 View Data

View(mydata)

reactable::reactable(data = mydata, sortable = TRUE, resizable = TRUE, filterable = TRUE, 
    searchable = TRUE, pagination = TRUE, paginationType = "numbers", showPageSizeOptions = TRUE, 
    highlight = TRUE, striped = TRUE, outlined = TRUE, compact = TRUE, wrap = FALSE, 
    showSortIcon = TRUE, showSortable = TRUE)

2.7.2 Overview / Exploratory Data Analysis (EDA)

Summary of Data via summarytools 📦

summarytools::view(summarytools::dfSummary(mydata %>% select(-keycolumns)))

if (!dir.exists(here::here("out"))) {
    dir.create(here::here("out"))
}

summarytools::view(x = summarytools::dfSummary(mydata %>% select(-keycolumns)), file = here::here("out", 
    "mydata_summary.html"))

Summary via dataMaid 📦

if (!dir.exists(here::here("out"))) {
    dir.create(here::here("out"))
}

dataMaid::makeDataReport(data = mydata, file = here::here("out", "dataMaid_mydata.Rmd"), 
    replace = TRUE, openResult = FALSE, render = FALSE, quiet = TRUE)

Summary via explore 📦

if (!dir.exists(here::here("out"))) {
    dir.create(here::here("out"))
}

mydata %>% select(-dateVariables) %>% explore::report(output_file = "mydata_report.html", 
    output_dir = here::here("out"))

Glimpse of Data

glimpse(mydata %>% select(-keycolumns, -dateVariables))

Observations: 250
Variables: 17
$ Sex                  <chr> "Male", "Male", "Female", "Male", "Female", "Mal…
$ Age                  <dbl> 54, 60, 30, 56, 45, 69, 61, 60, 39, 65, 63, 72, …
$ Race                 <chr> "Hispanic", "White", "White", "White", "White", …
$ PreinvasiveComponent <chr> "Absent", "Present", "Absent", "Present", "Prese…
$ LVI                  <chr> "Present", "Present", "Absent", "Present", "Abse…
$ PNI                  <chr> "Absent", "Absent", "Present", "Absent", "Presen…
$ Death                <lgl> TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE…
$ Group                <chr> "Control", "Control", "Control", "Control", "Con…
$ Grade                <chr> "2", "3", "3", "3", "2", "1", "1", "3", "1", "1"…
$ TStage               <chr> "3", "3", "2", "1", "4", "4", "4", "3", "2", "1"…
$ `Anti-X-intensity`   <dbl> 3, 2, 2, 3, 3, 2, 3, 3, 3, 3, 2, 2, 1, 2, 3, 1, …
$ `Anti-Y-intensity`   <dbl> 2, 2, 2, 2, 1, 2, 3, 1, 3, 2, 2, 3, 3, 1, 3, 3, …
$ LymphNodeMetastasis  <chr> "Absent", "Absent", "Absent", "Absent", "Absent"…
$ Valid                <lgl> FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, …
$ Smoker               <lgl> FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, T…
$ Grade_Level          <chr> "moderate", "high", "moderate", "moderate", "low…
$ DeathTime            <chr> "Within1Year", "Within1Year", "Within1Year", "Wi…

mydata %>% explore::describe()

               variable type na na_pct unique min  mean max
1                    ID  chr  0    0.0    250  NA    NA  NA
2                  Name  chr  1    0.4    250  NA    NA  NA
3                   Sex  chr  1    0.4      3  NA    NA  NA
4                   Age  dbl  1    0.4     50  25 50.39  73
5                  Race  chr  1    0.4      8  NA    NA  NA
6  PreinvasiveComponent  chr  1    0.4      3  NA    NA  NA
7                   LVI  chr  1    0.4      3  NA    NA  NA
8                   PNI  chr  1    0.4      3  NA    NA  NA
9      LastFollowUpDate  dat  1    0.4     13  NA    NA  NA
10                Death  lgl  1    0.4      3   0  0.71   1
11                Group  chr  1    0.4      3  NA    NA  NA
12                Grade  chr  1    0.4      4  NA    NA  NA
13               TStage  chr  1    0.4      5  NA    NA  NA
14     Anti-X-intensity  dbl  1    0.4      4   1  2.43   3
15     Anti-Y-intensity  dbl  1    0.4      4   1  2.06   3
16  LymphNodeMetastasis  chr  1    0.4      3  NA    NA  NA
17                Valid  lgl  1    0.4      3   0  0.45   1
18               Smoker  lgl  1    0.4      3   0  0.50   1
19          Grade_Level  chr  1    0.4      4  NA    NA  NA
20          SurgeryDate  dat  1    0.4    221  NA    NA  NA
21            DeathTime  chr  0    0.0      2  NA    NA  NA

Explore

explore::explore(mydata)

2.7.3 Control Data

Control Data if matching expectations

visdat::vis_expect(data = mydata, expectation = ~.x == -1, show_perc = TRUE)

visdat::vis_expect(mydata, ~.x >= 25)

See missing values

visdat::vis_miss(airquality, cluster = TRUE)

visdat::vis_miss(airquality, sort_miss = TRUE)

xray::anomalies(mydata)

$variables
               Variable   q qNA  pNA qZero pZero qBlank pBlank qInf pInf
1                 Valid 250   1 0.4%   137 54.8%      0      -    0    -
2                Smoker 250   1 0.4%   125   50%      0      -    0    -
3                 Death 250   1 0.4%    73 29.2%      0      -    0    -
4                   Sex 250   1 0.4%     0     -      0      -    0    -
5  PreinvasiveComponent 250   1 0.4%     0     -      0      -    0    -
6                   LVI 250   1 0.4%     0     -      0      -    0    -
7                   PNI 250   1 0.4%     0     -      0      -    0    -
8                 Group 250   1 0.4%     0     -      0      -    0    -
9   LymphNodeMetastasis 250   1 0.4%     0     -      0      -    0    -
10                Grade 250   1 0.4%     0     -      0      -    0    -
11     Anti-X-intensity 250   1 0.4%     0     -      0      -    0    -
12     Anti-Y-intensity 250   1 0.4%     0     -      0      -    0    -
13          Grade_Level 250   1 0.4%     0     -      0      -    0    -
14               TStage 250   1 0.4%     0     -      0      -    0    -
15                 Race 250   1 0.4%     0     -      0      -    0    -
16     LastFollowUpDate 250   1 0.4%     0     -      0      -    0    -
17                  Age 250   1 0.4%     0     -      0      -    0    -
18          SurgeryDate 250   1 0.4%     0     -      0      -    0    -
19                 Name 250   1 0.4%     0     -      0      -    0    -
20            DeathTime 250   0    -     0     -      0      -    0    -
21                   ID 250   0    -     0     -      0      -    0    -
   qDistinct      type anomalous_percent
1          3   Logical             55.2%
2          3   Logical             50.4%
3          3   Logical             29.6%
4          3 Character              0.4%
5          3 Character              0.4%
6          3 Character              0.4%
7          3 Character              0.4%
8          3 Character              0.4%
9          3 Character              0.4%
10         4 Character              0.4%
11         4   Numeric              0.4%
12         4   Numeric              0.4%
13         4 Character              0.4%
14         5 Character              0.4%
15         8 Character              0.4%
16        13 Timestamp              0.4%
17        50   Numeric              0.4%
18       221 Timestamp              0.4%
19       250 Character              0.4%
20         2 Character                 -
21       250 Character                 -

$problem_variables
 [1] Variable          q                 qNA               pNA              
 [5] qZero             pZero             qBlank            pBlank           
 [9] qInf              pInf              qDistinct         type             
[13] anomalous_percent problems         
<0 rows> (or 0-length row.names)

xray::distributions(mydata)

================================================================================

[1] "Ignoring variable LastFollowUpDate: Unsupported type for visualization."

[1] "Ignoring variable SurgeryDate: Unsupported type for visualization."

          Variable p_1 p_10 p_25 p_50 p_75 p_90 p_99
1 Anti-X-intensity   1    2    2    3    3    3    3
2 Anti-Y-intensity   1    1    1    2    3    3    3
3              Age  26   31   38   50   63   70   73

2.7.4 Explore Data

Summary of Data via DataExplorer 📦

DataExplorer::plot_str(mydata)

DataExplorer::plot_str(mydata, type = "r")

DataExplorer::introduce(mydata)

# A tibble: 1 x 9
   rows columns discrete_columns continuous_colu… all_missing_col…
  <int>   <int>            <int>            <int>            <int>
1   250      21               18                3                0
# … with 4 more variables: total_missing_values <int>, complete_rows <int>,
#   total_observations <int>, memory_usage <dbl>

DataExplorer::plot_intro(mydata)

DataExplorer::plot_missing(mydata)

Drop columns

mydata2 <- DataExplorer::drop_columns(mydata, "TStage")

DataExplorer::plot_bar(mydata)

DataExplorer::plot_bar(mydata, with = "Death")

DataExplorer::plot_histogram(mydata)

3 Statistical Analysis

Learn these tests as highlighted in (Schmidt et al. 2017).²¹

4 Results

Write results as described in (Knijn, Simmer, and Nagtegaal 2015)²²

Describe the number of patients included in the analysis and reason for dropout
Report patient/disease characteristics (including the biomarker of interest) with the number of missing values
Describe the interaction of the biomarker of interest with established prognostic variables
Include at least 90 % of initial cases included in univariate and multivariate analyses
Report the estimated effect (relative risk/odds ratio, confidence interval, and p value) in univariate analysis
Report the estimated effect (hazard rate/odds ratio, confidence interval, and p value) in multivariate analysis
Report the estimated effects (hazard ratio/odds ratio, confidence interval, and p value) of other prognostic factors included in multivariate analysis

4.1 Descriptive Statistics

Codes for Descriptive Statistics.²³

4.1.1 Table One

Report Data properties via report 📦

mydata %>% dplyr::select(-dplyr::contains("Date")) %>% report::report()

The data contains 250 observations of the following variables:
  - ID: 250 entries: 001, n = 1; 002, n = 1; 003, n = 1 and 247 others
  - Name: 249 entries: Aansh, n = 1; Abdurahmon, n = 1; Abrah, n = 1 and 246 others (1 missing)
  - Sex: 2 entries: Male, n = 126; Female, n = 123 (1 missing)
  - Age: Mean = 50.39, SD = 14.06, range = [25, 73], 1 missing
  - Race: 7 entries: White, n = 162; Hispanic, n = 39; Black, n = 26 and 4 others (1 missing)
  - PreinvasiveComponent: 2 entries: Absent, n = 186; Present, n = 63 (1 missing)
  - LVI: 2 entries: Absent, n = 157; Present, n = 92 (1 missing)
  - PNI: 2 entries: Absent, n = 171; Present, n = 78 (1 missing)
  - Death: 2 levels: FALSE (n = 73); TRUE (n = 176) and missing (n = 1)
  - Group: 2 entries: Control, n = 131; Treatment, n = 118 (1 missing)
  - Grade: 3 entries: 3, n = 101; 1, n = 83; 2, n = 65 (1 missing)
  - TStage: 4 entries: 4, n = 101; 3, n = 66; 2, n = 50 and 1 other (1 missing)
  - Anti-X-intensity: Mean = 2.43, SD = 0.64, range = [1, 3], 1 missing
  - Anti-Y-intensity: Mean = 2.06, SD = 0.78, range = [1, 3], 1 missing
  - LymphNodeMetastasis: 2 entries: Absent, n = 153; Present, n = 96 (1 missing)
  - Valid: 2 levels: FALSE (n = 137); TRUE (n = 112) and missing (n = 1)
  - Smoker: 2 levels: FALSE (n = 125); TRUE (n = 124) and missing (n = 1)
  - Grade_Level: 3 entries: high, n = 100; low, n = 77; moderate, n = 72 (1 missing)
  - DeathTime: 2 entries: Within1Year, n = 149; MoreThan1Year, n = 101

Table 1 via arsenal 📦

# cat(names(mydata), sep = ' + \n')
library(arsenal)
tab1 <- arsenal::tableby(~Sex + Age + Race + PreinvasiveComponent + LVI + PNI + Death + 
    Group + Grade + TStage + `Anti-X-intensity` + `Anti-Y-intensity` + LymphNodeMetastasis + 
    Valid + Smoker + Grade_Level, data = mydata)
summary(tab1)

	Overall (N=250)
Sex
N-Miss	1
Female	123 (49.4%)
Male	126 (50.6%)
Age
N-Miss	1
Mean (SD)	50.390 (14.057)
Range	25.000 - 73.000
Race
N-Miss	1
Asian	15 (6.0%)
Bi-Racial	4 (1.6%)
Black	26 (10.4%)
Hispanic	39 (15.7%)
Native	2 (0.8%)
Other	1 (0.4%)
White	162 (65.1%)
PreinvasiveComponent
N-Miss	1
Absent	186 (74.7%)
Present	63 (25.3%)
LVI
N-Miss	1
Absent	157 (63.1%)
Present	92 (36.9%)
PNI
N-Miss	1
Absent	171 (68.7%)
Present	78 (31.3%)
Death
N-Miss	1
FALSE	73 (29.3%)
TRUE	176 (70.7%)
Group
N-Miss	1
Control	131 (52.6%)
Treatment	118 (47.4%)
Grade
N-Miss	1
1	83 (33.3%)
2	65 (26.1%)
3	101 (40.6%)
TStage
N-Miss	1
1	32 (12.9%)
2	50 (20.1%)
3	66 (26.5%)
4	101 (40.6%)
Anti-X-intensity
N-Miss	1
Mean (SD)	2.430 (0.638)
Range	1.000 - 3.000
Anti-Y-intensity
N-Miss	1
Mean (SD)	2.060 (0.778)
Range	1.000 - 3.000
LymphNodeMetastasis
N-Miss	1
Absent	153 (61.4%)
Present	96 (38.6%)
Valid
N-Miss	1
FALSE	137 (55.0%)
TRUE	112 (45.0%)
Smoker
N-Miss	1
FALSE	125 (50.2%)
TRUE	124 (49.8%)
Grade_Level
N-Miss	1
high	100 (40.2%)
low	77 (30.9%)
moderate	72 (28.9%)

Table 1 via tableone 📦

library(tableone)
mydata %>% select(-keycolumns, -dateVariables) %>% tableone::CreateTableOne(data = .)

                                    
                                     Overall      
  n                                    250        
  Sex = Male (%)                       126 (50.6) 
  Age (mean (SD))                    50.39 (14.06)
  Race (%)                                        
     Asian                              15 ( 6.0) 
     Bi-Racial                           4 ( 1.6) 
     Black                              26 (10.4) 
     Hispanic                           39 (15.7) 
     Native                              2 ( 0.8) 
     Other                               1 ( 0.4) 
     White                             162 (65.1) 
  PreinvasiveComponent = Present (%)    63 (25.3) 
  LVI = Present (%)                     92 (36.9) 
  PNI = Present (%)                     78 (31.3) 
  Death = TRUE (%)                     176 (70.7) 
  Group = Treatment (%)                118 (47.4) 
  Grade (%)                                       
     1                                  83 (33.3) 
     2                                  65 (26.1) 
     3                                 101 (40.6) 
  TStage (%)                                      
     1                                  32 (12.9) 
     2                                  50 (20.1) 
     3                                  66 (26.5) 
     4                                 101 (40.6) 
  Anti-X-intensity (mean (SD))        2.43 (0.64) 
  Anti-Y-intensity (mean (SD))        2.06 (0.78) 
  LymphNodeMetastasis = Present (%)     96 (38.6) 
  Valid = TRUE (%)                     112 (45.0) 
  Smoker = TRUE (%)                    124 (49.8) 
  Grade_Level (%)                                 
     high                              100 (40.2) 
     low                                77 (30.9) 
     moderate                           72 (28.9) 
  DeathTime = Within1Year (%)          149 (59.6)

Descriptive Statistics of Continuous Variables

mydata %>% select(continiousVariables, numericVariables, integerVariables) %>% summarytools::descr(., 
    style = "rmarkdown")

print(summarytools::descr(mydata), method = "render", table.classes = "st-small")

mydata %>% summarytools::descr(., stats = "common", transpose = TRUE, headings = FALSE)

mydata %>% summarytools::descr(stats = "common") %>% summarytools::tb()

mydata$Sex %>% summarytools::freq(cumul = FALSE, report.nas = FALSE) %>% summarytools::tb()

mydata %>% explore::describe() %>% dplyr::filter(unique < 5)

               variable type na na_pct unique min mean max
1                   Sex  chr  1    0.4      3  NA   NA  NA
2  PreinvasiveComponent  chr  1    0.4      3  NA   NA  NA
3                   LVI  chr  1    0.4      3  NA   NA  NA
4                   PNI  chr  1    0.4      3  NA   NA  NA
5                 Death  lgl  1    0.4      3   0 0.71   1
6                 Group  chr  1    0.4      3  NA   NA  NA
7                 Grade  chr  1    0.4      4  NA   NA  NA
8      Anti-X-intensity  dbl  1    0.4      4   1 2.43   3
9      Anti-Y-intensity  dbl  1    0.4      4   1 2.06   3
10  LymphNodeMetastasis  chr  1    0.4      3  NA   NA  NA
11                Valid  lgl  1    0.4      3   0 0.45   1
12               Smoker  lgl  1    0.4      3   0 0.50   1
13          Grade_Level  chr  1    0.4      4  NA   NA  NA
14            DeathTime  chr  0    0.0      2  NA   NA  NA

mydata %>% explore::describe() %>% dplyr::filter(na > 0)

               variable type na na_pct unique min  mean max
1                  Name  chr  1    0.4    250  NA    NA  NA
2                   Sex  chr  1    0.4      3  NA    NA  NA
3                   Age  dbl  1    0.4     50  25 50.39  73
4                  Race  chr  1    0.4      8  NA    NA  NA
5  PreinvasiveComponent  chr  1    0.4      3  NA    NA  NA
6                   LVI  chr  1    0.4      3  NA    NA  NA
7                   PNI  chr  1    0.4      3  NA    NA  NA
8      LastFollowUpDate  dat  1    0.4     13  NA    NA  NA
9                 Death  lgl  1    0.4      3   0  0.71   1
10                Group  chr  1    0.4      3  NA    NA  NA
11                Grade  chr  1    0.4      4  NA    NA  NA
12               TStage  chr  1    0.4      5  NA    NA  NA
13     Anti-X-intensity  dbl  1    0.4      4   1  2.43   3
14     Anti-Y-intensity  dbl  1    0.4      4   1  2.06   3
15  LymphNodeMetastasis  chr  1    0.4      3  NA    NA  NA
16                Valid  lgl  1    0.4      3   0  0.45   1
17               Smoker  lgl  1    0.4      3   0  0.50   1
18          Grade_Level  chr  1    0.4      4  NA    NA  NA
19          SurgeryDate  dat  1    0.4    221  NA    NA  NA

mydata %>% explore::describe()

               variable type na na_pct unique min  mean max
1                    ID  chr  0    0.0    250  NA    NA  NA
2                  Name  chr  1    0.4    250  NA    NA  NA
3                   Sex  chr  1    0.4      3  NA    NA  NA
4                   Age  dbl  1    0.4     50  25 50.39  73
5                  Race  chr  1    0.4      8  NA    NA  NA
6  PreinvasiveComponent  chr  1    0.4      3  NA    NA  NA
7                   LVI  chr  1    0.4      3  NA    NA  NA
8                   PNI  chr  1    0.4      3  NA    NA  NA
9      LastFollowUpDate  dat  1    0.4     13  NA    NA  NA
10                Death  lgl  1    0.4      3   0  0.71   1
11                Group  chr  1    0.4      3  NA    NA  NA
12                Grade  chr  1    0.4      4  NA    NA  NA
13               TStage  chr  1    0.4      5  NA    NA  NA
14     Anti-X-intensity  dbl  1    0.4      4   1  2.43   3
15     Anti-Y-intensity  dbl  1    0.4      4   1  2.06   3
16  LymphNodeMetastasis  chr  1    0.4      3  NA    NA  NA
17                Valid  lgl  1    0.4      3   0  0.45   1
18               Smoker  lgl  1    0.4      3   0  0.50   1
19          Grade_Level  chr  1    0.4      4  NA    NA  NA
20          SurgeryDate  dat  1    0.4    221  NA    NA  NA
21            DeathTime  chr  0    0.0      2  NA    NA  NA

4.1.2 Categorical Variables

Use R/gc_desc_cat.R to generate gc_desc_cat.Rmd containing descriptive statistics for categorical variables

source(here::here("R", "gc_desc_cat.R"))

4.1.2.1 Descriptive Statistics Sex

mydata %>% janitor::tabyl(Sex) %>% janitor::adorn_pct_formatting(rounding = "half up", 
    digits = 1) %>% knitr::kable()

Sex	n	percent	valid_percent
Female	123	49.2%	49.4%
Male	126	50.4%	50.6%
NA	1	0.4%	-

4.1.2.2 Descriptive Statistics Race

mydata %>% janitor::tabyl(Race) %>% janitor::adorn_pct_formatting(rounding = "half up", 
    digits = 1) %>% knitr::kable()

Race	n	percent	valid_percent
Asian	15	6.0%	6.0%
Bi-Racial	4	1.6%	1.6%
Black	26	10.4%	10.4%
Hispanic	39	15.6%	15.7%
Native	2	0.8%	0.8%
Other	1	0.4%	0.4%
White	162	64.8%	65.1%
NA	1	0.4%	-

4.1.2.3 Descriptive Statistics PreinvasiveComponent

mydata %>% janitor::tabyl(PreinvasiveComponent) %>% janitor::adorn_pct_formatting(rounding = "half up", 
    digits = 1) %>% knitr::kable()

PreinvasiveComponent	n	percent	valid_percent
Absent	186	74.4%	74.7%
Present	63	25.2%	25.3%
NA	1	0.4%	-

4.1.2.4 Descriptive Statistics LVI

mydata %>% janitor::tabyl(LVI) %>% janitor::adorn_pct_formatting(rounding = "half up", 
    digits = 1) %>% knitr::kable()

LVI	n	percent	valid_percent
Absent	157	62.8%	63.1%
Present	92	36.8%	36.9%
NA	1	0.4%	-

4.1.2.5 Descriptive Statistics PNI

mydata %>% janitor::tabyl(PNI) %>% janitor::adorn_pct_formatting(rounding = "half up", 
    digits = 1) %>% knitr::kable()

PNI	n	percent	valid_percent
Absent	171	68.4%	68.7%
Present	78	31.2%	31.3%
NA	1	0.4%	-

4.1.2.6 Descriptive Statistics Group

mydata %>% janitor::tabyl(Group) %>% janitor::adorn_pct_formatting(rounding = "half up", 
    digits = 1) %>% knitr::kable()

Group	n	percent	valid_percent
Control	131	52.4%	52.6%
Treatment	118	47.2%	47.4%
NA	1	0.4%	-

4.1.2.7 Descriptive Statistics Grade

mydata %>% janitor::tabyl(Grade) %>% janitor::adorn_pct_formatting(rounding = "half up", 
    digits = 1) %>% knitr::kable()

Grade	n	percent	valid_percent
1	83	33.2%	33.3%
2	65	26.0%	26.1%
3	101	40.4%	40.6%
NA	1	0.4%	-

4.1.2.8 Descriptive Statistics TStage

mydata %>% janitor::tabyl(TStage) %>% janitor::adorn_pct_formatting(rounding = "half up", 
    digits = 1) %>% knitr::kable()

TStage	n	percent	valid_percent
1	32	12.8%	12.9%
2	50	20.0%	20.1%
3	66	26.4%	26.5%
4	101	40.4%	40.6%
NA	1	0.4%	-

4.1.2.9 Descriptive Statistics LymphNodeMetastasis

mydata %>% janitor::tabyl(LymphNodeMetastasis) %>% janitor::adorn_pct_formatting(rounding = "half up", 
    digits = 1) %>% knitr::kable()

LymphNodeMetastasis	n	percent	valid_percent
Absent	153	61.2%	61.4%
Present	96	38.4%	38.6%
NA	1	0.4%	-

4.1.2.10 Descriptive Statistics Grade_Level

mydata %>% janitor::tabyl(Grade_Level) %>% janitor::adorn_pct_formatting(rounding = "half up", 
    digits = 1) %>% knitr::kable()

Grade_Level	n	percent	valid_percent
high	100	40.0%	40.2%
low	77	30.8%	30.9%
moderate	72	28.8%	28.9%
NA	1	0.4%	-

4.1.2.11 Descriptive Statistics DeathTime

mydata %>% janitor::tabyl(DeathTime) %>% janitor::adorn_pct_formatting(rounding = "half up", 
    digits = 1) %>% knitr::kable()

DeathTime	n	percent
MoreThan1Year	101	40.4%
Within1Year	149	59.6%

race_stats <- summarytools::freq(mydata$Race)
print(race_stats, report.nas = FALSE, totals = FALSE, display.type = FALSE, Variable.label = "Race Group")

mydata %>% explore::describe(PreinvasiveComponent)

variable = PreinvasiveComponent
type     = character
na       = 1 of 250 (0.4%)
unique   = 3
 Absent  = 186 (74.4%)
 Present = 63 (25.2%)
 NA      = 1 (0.4%)

## Frequency or custom tables for categorical variables
SmartEDA::ExpCTable(mydata, Target = NULL, margin = 1, clim = 10, nlim = 5, round = 2, 
    bin = NULL, per = T)

               Variable         Valid Frequency Percent CumPercent
1                   Sex        Female       123    49.2       49.2
2                   Sex          Male       126    50.4       99.6
3                   Sex            NA         1     0.4      100.0
4                   Sex         TOTAL       250      NA         NA
5                  Race         Asian        15     6.0        6.0
6                  Race     Bi-Racial         4     1.6        7.6
7                  Race         Black        26    10.4       18.0
8                  Race      Hispanic        39    15.6       33.6
9                  Race            NA         1     0.4       34.0
10                 Race        Native         2     0.8       34.8
11                 Race         Other         1     0.4       35.2
12                 Race         White       162    64.8      100.0
13                 Race         TOTAL       250      NA         NA
14 PreinvasiveComponent        Absent       186    74.4       74.4
15 PreinvasiveComponent            NA         1     0.4       74.8
16 PreinvasiveComponent       Present        63    25.2      100.0
17 PreinvasiveComponent         TOTAL       250      NA         NA
18                  LVI        Absent       157    62.8       62.8
19                  LVI            NA         1     0.4       63.2
20                  LVI       Present        92    36.8      100.0
21                  LVI         TOTAL       250      NA         NA
22                  PNI        Absent       171    68.4       68.4
23                  PNI            NA         1     0.4       68.8
24                  PNI       Present        78    31.2      100.0
25                  PNI         TOTAL       250      NA         NA
26                Group       Control       131    52.4       52.4
27                Group            NA         1     0.4       52.8
28                Group     Treatment       118    47.2      100.0
29                Group         TOTAL       250      NA         NA
30                Grade             1        83    33.2       33.2
31                Grade             2        65    26.0       59.2
32                Grade             3       101    40.4       99.6
33                Grade            NA         1     0.4      100.0
34                Grade         TOTAL       250      NA         NA
35               TStage             1        32    12.8       12.8
36               TStage             2        50    20.0       32.8
37               TStage             3        66    26.4       59.2
38               TStage             4       101    40.4       99.6
39               TStage            NA         1     0.4      100.0
40               TStage         TOTAL       250      NA         NA
41  LymphNodeMetastasis        Absent       153    61.2       61.2
42  LymphNodeMetastasis            NA         1     0.4       61.6
43  LymphNodeMetastasis       Present        96    38.4      100.0
44  LymphNodeMetastasis         TOTAL       250      NA         NA
45          Grade_Level          high       100    40.0       40.0
46          Grade_Level           low        77    30.8       70.8
47          Grade_Level      moderate        72    28.8       99.6
48          Grade_Level            NA         1     0.4      100.0
49          Grade_Level         TOTAL       250      NA         NA
50            DeathTime MoreThan1Year       101    40.4       40.4
51            DeathTime   Within1Year       149    59.6      100.0
52            DeathTime         TOTAL       250      NA         NA
53     Anti-X-intensity             1        20     8.0        8.0
54     Anti-X-intensity             2       102    40.8       48.8
55     Anti-X-intensity             3       127    50.8       99.6
56     Anti-X-intensity            NA         1     0.4      100.0
57     Anti-X-intensity         TOTAL       250      NA         NA
58     Anti-Y-intensity             1        68    27.2       27.2
59     Anti-Y-intensity             2        98    39.2       66.4
60     Anti-Y-intensity             3        83    33.2       99.6
61     Anti-Y-intensity            NA         1     0.4      100.0
62     Anti-Y-intensity         TOTAL       250      NA         NA

inspectdf::inspect_cat(mydata)

# A tibble: 16 x 5
   col_name               cnt common      common_pcnt levels            
   <chr>                <int> <chr>             <dbl> <named list>      
 1 Death                    3 TRUE               70.4 <tibble [3 × 3]>  
 2 DeathTime                2 Within1Year        59.6 <tibble [2 × 3]>  
 3 Grade                    4 3                  40.4 <tibble [4 × 3]>  
 4 Grade_Level              4 high               40   <tibble [4 × 3]>  
 5 Group                    3 Control            52.4 <tibble [3 × 3]>  
 6 ID                     250 001                 0.4 <tibble [250 × 3]>
 7 LVI                      3 Absent             62.8 <tibble [3 × 3]>  
 8 LymphNodeMetastasis      3 Absent             61.2 <tibble [3 × 3]>  
 9 Name                   250 Aansh               0.4 <tibble [250 × 3]>
10 PNI                      3 Absent             68.4 <tibble [3 × 3]>  
11 PreinvasiveComponent     3 Absent             74.4 <tibble [3 × 3]>  
12 Race                     8 White              64.8 <tibble [8 × 3]>  
13 Sex                      3 Male               50.4 <tibble [3 × 3]>  
14 Smoker                   3 FALSE              50   <tibble [3 × 3]>  
15 TStage                   5 4                  40.4 <tibble [5 × 3]>  
16 Valid                    3 FALSE              54.8 <tibble [3 × 3]>

inspectdf::inspect_cat(mydata)$levels$Group

# A tibble: 3 x 3
  value      prop   cnt
  <chr>     <dbl> <int>
1 Control   0.524   131
2 Treatment 0.472   118
3 <NA>      0.004     1

4.1.2.12 Split-Group Stats Categorical

library(summarytools)

grouped_freqs <- stby(data = mydata$Smoker, INDICES = mydata$Sex, FUN = freq, cumul = FALSE, 
    report.nas = FALSE)

grouped_freqs %>% tb(order = 2)

4.1.2.13 Grouped Categorical

summarytools::stby(list(x = mydata$LVI, y = mydata$LymphNodeMetastasis), mydata$PNI, 
    summarytools::ctable)

with(mydata, summarytools::stby(list(x = LVI, y = LymphNodeMetastasis), PNI, summarytools::ctable))

SmartEDA::ExpCTable(mydata, Target = "Sex", margin = 1, clim = 10, nlim = NULL, round = 2, 
    bin = 4, per = F)

               VARIABLE      CATEGORY Sex:Female Sex:Male Sex:NA TOTAL
1                  Race         Asian         10        5      0    15
2                  Race     Bi-Racial          2        2      0     4
3                  Race         Black         11       15      0    26
4                  Race      Hispanic         22       17      0    39
5                  Race            NA          0        1      0     1
6                  Race        Native          1        1      0     2
7                  Race         Other          1        0      0     1
8                  Race         White         76       85      1   162
9                  Race         TOTAL        123      126      1   250
10 PreinvasiveComponent        Absent         91       94      1   186
11 PreinvasiveComponent            NA          1        0      0     1
12 PreinvasiveComponent       Present         31       32      0    63
13 PreinvasiveComponent         TOTAL        123      126      1   250
14                  LVI        Absent         84       72      1   157
15                  LVI            NA          0        1      0     1
16                  LVI       Present         39       53      0    92
17                  LVI         TOTAL        123      126      1   250
18                  PNI        Absent         78       93      0   171
19                  PNI            NA          0        0      1     1
20                  PNI       Present         45       33      0    78
21                  PNI         TOTAL        123      126      1   250
22                Group       Control         71       60      0   131
23                Group            NA          1        0      0     1
24                Group     Treatment         51       66      1   118
25                Group         TOTAL        123      126      1   250
26                Grade             1         36       47      0    83
27                Grade             2         37       27      1    65
28                Grade             3         50       51      0   101
29                Grade            NA          0        1      0     1
30                Grade         TOTAL        123      126      1   250
31               TStage             1         14       18      0    32
32               TStage             2         25       25      0    50
33               TStage             3         28       38      0    66
34               TStage             4         56       44      1   101
35               TStage            NA          0        1      0     1
36               TStage         TOTAL        123      126      1   250
37  LymphNodeMetastasis        Absent         76       76      1   153
38  LymphNodeMetastasis            NA          1        0      0     1
39  LymphNodeMetastasis       Present         46       50      0    96
40  LymphNodeMetastasis         TOTAL        123      126      1   250
41          Grade_Level          high         51       48      1   100
42          Grade_Level           low         35       42      0    77
43          Grade_Level      moderate         36       36      0    72
44          Grade_Level            NA          1        0      0     1
45          Grade_Level         TOTAL        123      126      1   250
46            DeathTime MoreThan1Year         44       57      0   101
47            DeathTime   Within1Year         79       69      1   149
48            DeathTime         TOTAL        123      126      1   250
49     Anti-X-intensity             1          7       13      0    20
50     Anti-X-intensity             2         54       47      1   102
51     Anti-X-intensity             3         61       66      0   127
52     Anti-X-intensity            NA          1        0      0     1
53     Anti-X-intensity         TOTAL        123      126      1   250
54     Anti-Y-intensity             1         36       32      0    68
55     Anti-Y-intensity             2         50       48      0    98
56     Anti-Y-intensity             3         36       46      1    83
57     Anti-Y-intensity            NA          1        0      0     1
58     Anti-Y-intensity         TOTAL        123      126      1   250

mydata %>% select(characterVariables) %>% select(PreinvasiveComponent, PNI, LVI) %>% 
    reactable::reactable(data = ., groupBy = c("PreinvasiveComponent", "PNI"), columns = list(LVI = reactable::colDef(aggregate = "count")))

4.1.3 Continious Variables

questionr:::icut()

source(here::here("R", "gc_desc_cont.R"))

Descriptive Statistics Age

mydata %>% jmv::descriptives(data = ., vars = "Age", hist = TRUE, dens = TRUE, box = TRUE, 
    violin = TRUE, dot = TRUE, mode = TRUE, sd = TRUE, variance = TRUE, skew = TRUE, 
    kurt = TRUE, quart = TRUE)


 DESCRIPTIVES

 Descriptives                       
 ────────────────────────────────── 
                          Age       
 ────────────────────────────────── 
   N                          249   
   Missing                      1   
   Mean                      50.4   
   Median                    50.0   
   Mode                      72.0   
   Standard deviation        14.1   
   Variance                   198   
   Minimum                   25.0   
   Maximum                   73.0   
   Skewness               -0.0364   
   Std. error skewness      0.154   
   Kurtosis                 -1.17   
   Std. error kurtosis      0.307   
   25th percentile           38.0   
   50th percentile           50.0   
   75th percentile           63.0   
 ──────────────────────────────────

Descriptive Statistics Anti-X-intensity

mydata %>% jmv::descriptives(data = ., vars = "Anti-X-intensity", hist = TRUE, dens = TRUE, 
    box = TRUE, violin = TRUE, dot = TRUE, mode = TRUE, sd = TRUE, variance = TRUE, 
    skew = TRUE, kurt = TRUE, quart = TRUE)


 DESCRIPTIVES

 Descriptives                                
 ─────────────────────────────────────────── 
                          Anti-X-intensity   
 ─────────────────────────────────────────── 
   N                                   249   
   Missing                               1   
   Mean                               2.43   
   Median                             3.00   
   Mode                               3.00   
   Standard deviation                0.638   
   Variance                          0.407   
   Minimum                            1.00   
   Maximum                            3.00   
   Skewness                         -0.672   
   Std. error skewness               0.154   
   Kurtosis                         -0.535   
   Std. error kurtosis               0.307   
   25th percentile                    2.00   
   50th percentile                    3.00   
   75th percentile                    3.00   
 ───────────────────────────────────────────

Descriptive Statistics Anti-Y-intensity

mydata %>% jmv::descriptives(data = ., vars = "Anti-Y-intensity", hist = TRUE, dens = TRUE, 
    box = TRUE, violin = TRUE, dot = TRUE, mode = TRUE, sd = TRUE, variance = TRUE, 
    skew = TRUE, kurt = TRUE, quart = TRUE)


 DESCRIPTIVES

 Descriptives                                
 ─────────────────────────────────────────── 
                          Anti-Y-intensity   
 ─────────────────────────────────────────── 
   N                                   249   
   Missing                               1   
   Mean                               2.06   
   Median                             2.00   
   Mode                               2.00   
   Standard deviation                0.778   
   Variance                          0.605   
   Minimum                            1.00   
   Maximum                            3.00   
   Skewness                         -0.105   
   Std. error skewness               0.154   
   Kurtosis                          -1.34   
   Std. error kurtosis               0.307   
   25th percentile                    1.00   
   50th percentile                    2.00   
   75th percentile                    3.00   
 ───────────────────────────────────────────

tab <- tableone::CreateTableOne(data = mydata)
# ?print.ContTable
tab$ContTable

                              
                               Overall      
  n                            250          
  Age (mean (SD))              50.39 (14.06)
  Anti-X-intensity (mean (SD))  2.43 (0.64) 
  Anti-Y-intensity (mean (SD))  2.06 (0.78)

print(tab$ContTable, nonnormal = c("Anti-X-intensity"))

                                 
                                  Overall           
  n                               250               
  Age (mean (SD))                 50.39 (14.06)     
  Anti-X-intensity (median [IQR])  3.00 [2.00, 3.00]
  Anti-Y-intensity (mean (SD))     2.06 (0.78)

mydata %>% explore::describe(Age)

variable = Age
type     = double
na       = 1 of 250 (0.4%)
unique   = 50
min|max  = 25 | 73
q05|q95  = 28 | 72
q25|q75  = 38 | 63
median   = 50
mean     = 50.38956

mydata %>% select(continiousVariables) %>% SmartEDA::ExpNumStat(data = ., by = "A", 
    gp = NULL, Qnt = seq(0, 1, 0.1), MesofShape = 2, Outlier = TRUE, round = 2)

inspectdf::inspect_num(mydata, breaks = 10)

# A tibble: 3 x 10
  col_name        min    q1 median  mean    q3   max     sd pcnt_na hist        
  <chr>         <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <named list>
1 Age              25    38     50 50.4     63    73 14.1       0.4 <tibble [12…
2 Anti-X-inten…     1     2      3  2.43     3     3  0.638     0.4 <tibble [12…
3 Anti-Y-inten…     1     1      2  2.06     3     3  0.778     0.4 <tibble [12…

inspectdf::inspect_num(mydata)$hist$Age

# A tibble: 27 x 2
   value         prop
   <chr>        <dbl>
 1 [-Inf, 24) 0      
 2 [24, 26)   0.00803
 3 [26, 28)   0.0402 
 4 [28, 30)   0.0201 
 5 [30, 32)   0.0442 
 6 [32, 34)   0.0321 
 7 [34, 36)   0.0442 
 8 [36, 38)   0.0402 
 9 [38, 40)   0.0482 
10 [40, 42)   0.0361 
# … with 17 more rows

inspectdf::inspect_num(mydata, breaks = 10) %>% inspectdf::show_plot()

4.1.3.1 Split-Group Stats Continious

grouped_descr <- summarytools::stby(data = mydata, INDICES = mydata$Sex, FUN = summarytools::descr, 
    stats = "common")
# grouped_descr %>% summarytools::tb(order = 2)
grouped_descr %>% summarytools::tb()

4.1.3.2 Grouped Continious

summarytools::stby(data = mydata, INDICES = mydata$PreinvasiveComponent, FUN = summarytools::descr, 
    stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE)

with(mydata, summarytools::stby(Age, PreinvasiveComponent, summarytools::descr), 
    stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE)

mydata %>% group_by(PreinvasiveComponent) %>% summarytools::descr(stats = "fivenum")

## Summary statistics by – category
SmartEDA::ExpNumStat(mydata, by = "GA", gp = "PreinvasiveComponent", Qnt = seq(0, 
    1, 0.1), MesofShape = 2, Outlier = TRUE, round = 2)

  Vname                        Group  TN nNeg nZero nPos NegInf PosInf NA_Value
1   Age     PreinvasiveComponent:All 250    0     0  249      0      0        1
2   Age  PreinvasiveComponent:Absent 186    0     0  185      0      0        1
3   Age PreinvasiveComponent:Present  63    0     0   63      0      0        0
4   Age      PreinvasiveComponent:NA   0    0     0    0      0      0        0
  Per_of_Missing   sum min  max  mean median    SD   CV  IQR Skewness Kurtosis
1           0.40 12547  25   73 50.39     50 14.06 0.28 25.0    -0.04    -1.17
2           0.54  9357  25   73 50.58     50 14.16 0.28 24.0    -0.05    -1.18
3           0.00  3150  25   73 50.00     50 13.89 0.28 22.5    -0.04    -1.16
4            NaN     0 Inf -Inf   NaN     NA    NA   NA   NA      NaN      NaN
  0%  10% 20%  30%  40% 50%  60%  70%  80% 90% 100% LB.25% UB.75% nOutliers
1 25 31.0  36 40.4 46.2  50 54.8 60.0 65.0  70   73   0.50 100.50         0
2 25 31.0  36 42.0 46.6  50 55.0 60.8 65.0  70   73   3.00  99.00         0
3 25 31.4  36 39.2 46.8  50 54.2 60.0 63.8  69   73   4.25  94.25         0
4 NA   NA  NA   NA   NA  NA   NA   NA   NA  NA   NA     NA     NA         0

4.2 Survival Analysis

Codes for Survival Analysis²⁴

Survival analysis with strata, clusters, frailties and competing risks in in Finalfit

https://www.datasurg.net/2019/09/12/survival-analysis-with-strata-clusters-frailties-and-competing-risks-in-in-finalfit/

Intracranial WHO grade I meningioma: a competing risk analysis of progression and disease-specific survival

https://link.springer.com/article/10.1007/s00701-019-04096-9

Calculate survival time

mydata$int <- lubridate::interval(lubridate::ymd(mydata$SurgeryDate), lubridate::ymd(mydata$LastFollowUpDate))
mydata$OverallTime <- lubridate::time_length(mydata$int, "month")
mydata$OverallTime <- round(mydata$OverallTime, digits = 1)

recode death status outcome as numbers for survival analysis

## Recoding mydata$Death into mydata$Outcome
mydata$Outcome <- forcats::fct_recode(as.character(mydata$Death), `1` = "TRUE", `0` = "FALSE")
mydata$Outcome <- as.numeric(as.character(mydata$Outcome))

it is always a good practice to double-check after recoding²⁵

table(mydata$Death, mydata$Outcome)

       
          0   1
  FALSE  73   0
  TRUE    0 176

4.2.1 Kaplan-Meier

library(survival)
# data(lung) km <- with(lung, Surv(time, status))
km <- with(mydata, Surv(OverallTime, Outcome))
head(km, 80)

 [1] 10.0   9.7   3.3  11.0  10.0+  3.7   5.4  11.2+ 10.1   7.6+ 10.2+  4.0 
[13] 10.7   9.8   8.3   8.8   4.9   7.3?  6.6   4.7  10.5+  6.6   6.3  10.7 
[25]  5.2+ 10.9   8.5   9.8   7.1   7.8   9.5  11.6+  7.8   5.3+  4.5+  4.7 
[37]  6.7   9.5+  5.4  10.4   6.3   6.3   5.4   5.0+  3.3   3.5  11.8+  9.8 
[49]  5.9   5.9  10.0   5.3   6.0   9.7   8.0+  8.9   3.2   7.1   7.3+  5.3 
[61]  3.8   5.7   5.9+  3.3  11.8   6.5   3.4+ 11.2   8.9  11.1+  6.4+  9.2+
[73]  7.0   8.9   6.2   7.9   9.0+  5.6+  7.6+ 11.0

plot(km)

Kaplan-Meier Plot Log-Rank Test

# Drawing Survival Curves Using ggplot2
# https://rpkgs.datanovia.com/survminer/reference/ggsurvplot.html
dependentKM <- "Surv(OverallTime, Outcome)"
explanatoryKM <- "LVI"

mydata %>%
  finalfit::surv_plot(.data = .,
                      dependent = dependentKM,
                      explanatory = explanatoryKM,
                      xlab='Time (months)',
                      pval=TRUE,
                      legend = 'none',
                      break.time.by = 12,
                      xlim = c(0,60)
                      # legend.labs = c('a','b')
                      )

# Drawing Survival Curves Using ggplot2
# https://rpkgs.datanovia.com/survminer/reference/ggsurvplot.html

mydata %>%
  finalfit::surv_plot(.data = .,
                      dependent = "Surv(OverallTime, Outcome)",
                      explanatory = "LVI",
                      xlab='Time (months)',
                      pval=TRUE,
                      legend = 'none',
                      break.time.by = 12,
                      xlim = c(0,60)
                      # legend.labs = c('a','b')
                      )

4.2.2 Univariate Cox-Regression

library(finalfit)
library(survival)
explanatoryUni <- "LVI"
dependentUni <- "Surv(OverallTime, Outcome)"

tUni <- mydata %>% finalfit::finalfit(dependentUni, explanatoryUni)

knitr::kable(tUni, row.names = FALSE, align = c("l", "l", "r", "r", "r", "r"))

Dependent: Surv(OverallTime, Outcome)		all	HR (univariable)	HR (multivariable)
LVI	Absent	157 (100.0)	NA	NA
	Present	92 (100.0)	2.02 (1.47-2.78, p<0.001)	2.02 (1.47-2.78, p<0.001)

tUni_df <- tibble::as_tibble(tUni, .name_repair = "minimal") %>% janitor::clean_names()

tUni_df_descr <- paste0("When ", tUni_df$dependent_surv_overall_time_outcome[1], 
    " is ", tUni_df$x[2], ", there is ", tUni_df$hr_univariable[2], " times risk than ", 
    "when ", tUni_df$dependent_surv_overall_time_outcome[1], " is ", tUni_df$x[1], 
    ".")

When LVI is Present, there is 2.02 (1.47-2.78, p<0.001) times risk than when LVI is Absent.

4.2.3 Kaplan-Meier Median Survival

km_fit <- survfit(Surv(OverallTime, Outcome) ~ LVI, data = mydata)
km_fit

Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)

   3 observations deleted due to missingness 
              n events median 0.95LCL 0.95UCL
LVI=Absent  157    111   22.6    15.3    29.4
LVI=Present  90     64    9.8     8.7    13.3

plot(km_fit)

# summary(km_fit)

km_fit_median_df <- summary(km_fit)
km_fit_median_df <- as.data.frame(km_fit_median_df$table) %>% janitor::clean_names() %>% 
    tibble::rownames_to_column()

km_fit
broom::tidy(km_fit)

km_fit_median_definition <- km_fit_median_df %>% dplyr::mutate(description = glue::glue("When {rowname}, median survival is {median} [{x0_95lcl} - {x0_95ucl}, 95% CI] months.")) %>% 
    dplyr::select(description) %>% pull()

When LVI=Absent, median survival is 22.6 [15.3 - 29.4, 95% CI] months., When LVI=Present, median survival is 9.8 [8.7 - 13.3, 95% CI] months.

4.2.4 1-3-5-yr survival

summary(km_fit, times = c(12, 36, 60))

Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)

3 observations deleted due to missingness 
                LVI=Absent 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     80      54    0.623  0.0408        0.548        0.708
   36     21      42    0.241  0.0412        0.172        0.337

                LVI=Present 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     17      47   0.3630  0.0597       0.2629        0.501
   36      2      15   0.0427  0.0292       0.0112        0.163

km_fit_summary <- summary(km_fit, times = c(12, 36, 60))

km_fit_df <- as.data.frame(km_fit_summary[c("strata", "time", "n.risk", "n.event", 
    "surv", "std.err", "lower", "upper")])

km_fit_definition <- km_fit_df %>% dplyr::mutate(description = glue::glue("When {strata}, {time} month survival is {scales::percent(surv)} [{scales::percent(lower)}-{scales::percent(upper)}, 95% CI].")) %>% 
    dplyr::select(description) %>% pull()

When LVI=Absent, 12 month survival is 62% [54.8%-71%, 95% CI]., When LVI=Absent, 36 month survival is 24% [17.2%-34%, 95% CI]., When LVI=Present, 12 month survival is 36% [26.3%-50%, 95% CI]., When LVI=Present, 36 month survival is 4% [1.1%-16%, 95% CI].

4.2.5 Pairwise comparison

dependentKM <- "Surv(OverallTime, Outcome)"
explanatoryKM <- "TStage"

mydata %>%
  finalfit::surv_plot(.data = .,
                      dependent = dependentKM,
                      explanatory = explanatoryKM,
                      xlab='Time (months)',
                      pval=TRUE,
                      legend = 'none',
                      break.time.by = 12,
                      xlim = c(0,60)
                      # legend.labs = c('a','b')
                      )

4.2.6 Multivariate Analysis Survival

5 Discussion

Interpret the results in context of the working hypothesis elaborated in the introduction and other relevant studies; include a discussion of limitations of the study.
Discuss potential clinical applications and implications for future research

6 Footer

Codes for explaining the software and the packages that are used in the analysis²⁶

6.0.1 Save Final Data

projectName <- list.files(path = here::here(), pattern = "Rproj")
projectName <- gsub(pattern = ".Rproj", replacement = "", x = projectName)

analysisDate <- as.character(Sys.Date())

imageName <- paste0(projectName, analysisDate, ".RData")

save.image(file = here::here("data", imageName))

rdsName <- paste0(projectName, analysisDate, ".rds")

readr::write_rds(x = mydata, path = here::here("data", rdsName))

saveRDS(object = mydata, file = here::here("data", rdsName))

excelName <- paste0(projectName, analysisDate, ".xlsx")

rio::export(x = mydata, file = here::here("data", excelName), format = "xlsx")

# writexl::write_xlsx(mydata, here::here('data', excelName))

print(glue::glue("saved data after analysis to ", rownames(file.info(here::here("data", 
    excelName))), " : ", as.character(file.info(here::here("data", excelName))$ctime)))

saved data after analysis to /Users/serdarbalciold/histopathology-template/data/histopathology-template2019-11-27.xlsx : 2019-11-27 12:00:40

6.0.2 Final Data Summary

# use summarytools to generate final data summary
# summarytools::view(summarytools::dfSummary(x = mydata
#                                            , style = "markdown"))

6.0.3 Software and Libraries Used

Why and how to cite software and packages?²⁷

citation()


To cite R in publications use:

  R Core Team (2019). R: A language and environment for statistical
  computing. R Foundation for Statistical Computing, Vienna, Austria.
  URL https://www.R-project.org/.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2019},
    url = {https://www.R-project.org/},
  }

We have invested a lot of time and effort in creating R, please cite it
when using it for data analysis. See also 'citation("pkgname")' for
citing R packages.

The jamovi project (2019). jamovi. (Version 0.9) [Computer Software]. Retrieved from https://www.jamovi.org.
R Core Team (2018). R: A Language and envionment for statistical computing. [Computer software]. Retrieved from https://cran.r-project.org/.
Fox, J., & Weisberg, S. (2018). car: Companion to Applied Regression. [R package]. Retrieved from https://cran.r-project.org/package=car. Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686 Data processing was carried out with R (R Core Team, 2019) and the easystats ecosystem (Lüdecke, Waggoner, & Makowski, 2019; Makowski, Ben-Shachar, & Lüdecke, 2019)

report::cite_packages(session = sessionInfo())

6.1 data[order(data$References), ]

Ethan Heinzen, Jason Sinnwell, Elizabeth Atkinson, Tina Gunderson and Gregory Dougherty (2019). arsenal: An Arsenal of ‘R’ Functions for Large-Scale Statistical Summaries. R package version 3.3.0. https://CRAN.R-project.org/package=arsenal
Ewen Harrison, Tom Drake and Riinu Ots (2019). finalfit: Quickly Create Elegant Regression Results Tables and Plots when Modelling. R package version 0.9.6. https://github.com/ewenharrison/finalfit
Hadley Wickham and Jennifer Bryan (2019). readxl: Read Excel Files. R package version 1.3.1. https://CRAN.R-project.org/package=readxl
Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2019). dplyr: A Grammar of Data Manipulation. R package version 0.8.3. https://CRAN.R-project.org/package=dplyr
Jim Hester and Gábor Csárdi (2019). pak: Another Approach to Package Installation. R package version 0.1.2. https://CRAN.R-project.org/package=pak
Jim Hester, Gábor Csárdi, Hadley Wickham, Winston Chang, Martin Morgan and Dan Tenenbaum (2019). remotes: R Package Installation from Remote Repositories, Including ‘GitHub’. R package version 2.1.0. https://CRAN.R-project.org/package=remotes Kazuki Yoshida (2019). tableone: Create ‘Table 1’ to Describe Baseline Characteristics. R package version 0.10.0. https://CRAN.R-project.org/package=tableone
Kirill Müller (2017). here: A Simpler Way to Find Your Files. R package version 0.1. https://CRAN.R-project.org/package=here
Makowski, D. & Lüdecke, D. (2019). The report package for R: Ensuring the use of best practices for results reporting. CRAN. Available from https://github.com/easystats/report. doi: .
Rinker, T. W. & Kurkiewicz, D. (2017). pacman: Package Management for R. version 0.5.0. Buffalo, New York. http://github.com/trinker/pacman
Stefan Milton Bache and Hadley Wickham (2014). magrittr: A Forward-Pipe Operator for R. R package version 1.5. https://CRAN.R-project.org/package=magrittr
Therneau T (2015). A Package for Survival Analysis in S. version2.38, <URL: https://CRAN.R-project.org/package=survival>.

report::show_packages(session = sessionInfo()) %>% kableExtra::kable()

citation("tidyverse")


  Wickham et al., (2019). Welcome to the tidyverse. Journal of Open
  Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686

A BibTeX entry for LaTeX users is

  @Article{,
    title = {Welcome to the {tidyverse}},
    author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani},
    year = {2019},
    journal = {Journal of Open Source Software},
    volume = {4},
    number = {43},
    pages = {1686},
    doi = {10.21105/joss.01686},
  }

citation("readxl")


To cite package 'readxl' in publications use:

  Hadley Wickham and Jennifer Bryan (2019). readxl: Read Excel Files. R
  package version 1.3.1. https://CRAN.R-project.org/package=readxl

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {readxl: Read Excel Files},
    author = {Hadley Wickham and Jennifer Bryan},
    year = {2019},
    note = {R package version 1.3.1},
    url = {https://CRAN.R-project.org/package=readxl},
  }

citation("janitor")


To cite package 'janitor' in publications use:

  Sam Firke (2019). janitor: Simple Tools for Examining and Cleaning
  Dirty Data. R package version 1.2.0.
  https://CRAN.R-project.org/package=janitor

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {janitor: Simple Tools for Examining and Cleaning Dirty Data},
    author = {Sam Firke},
    year = {2019},
    note = {R package version 1.2.0},
    url = {https://CRAN.R-project.org/package=janitor},
  }

citation("report")


To cite in publications use:

  Makowski, D. & Lüdecke, D. (2019). The report package for R: Ensuring
  the use of best practices for results reporting. CRAN. Available from
  https://github.com/easystats/report. doi: .

A BibTeX entry for LaTeX users is

  @Article{,
    title = {The report package for R: Ensuring the use of best practices for results reporting},
    author = {{Makowski} and {Dominique} and {Lüdecke} and {Daniel}},
    journal = {CRAN},
    year = {2019},
    note = {R package},
    url = {https://github.com/easystats/report},
  }

citation("finalfit")


To cite package 'finalfit' in publications use:

  Ewen Harrison, Tom Drake and Riinu Ots (2019). finalfit: Quickly
  Create Elegant Regression Results Tables and Plots when Modelling. R
  package version 0.9.6. https://github.com/ewenharrison/finalfit

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {finalfit: Quickly Create Elegant Regression Results Tables and Plots when
Modelling},
    author = {Ewen Harrison and Tom Drake and Riinu Ots},
    year = {2019},
    note = {R package version 0.9.6},
    url = {https://github.com/ewenharrison/finalfit},
  }

# citation('ggstatsplot')

if (!dir.exists(here::here("bib"))) {
    dir.create(here::here("bib"))
}

knitr::write_bib(x = c(.packages(), "knitr", "shiny"), file = here::here("bib", "packages.bib"))

6.1.1 Session Info

sessionInfo()

R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.15.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] finalfit_0.9.6  survival_3.1-6  tableone_0.10.0 arsenal_3.3.0  
 [5] readxl_1.3.1    magrittr_1.5    dplyr_0.8.3     report_0.1.0   
 [9] here_0.1        pak_0.1.2       pacman_0.5.1    remotes_2.1.0  

loaded via a namespace (and not attached):
  [1] backports_1.1.5        Hmisc_4.3-0            plyr_1.8.4            
  [4] dataMaid_1.3.2         igraph_1.2.4.1         lazyeval_0.2.2        
  [7] splines_3.6.0          ggplot2_3.2.1          digest_0.6.22         
 [10] foreach_1.4.7          htmltools_0.4.0        xray_0.2              
 [13] fansi_0.4.0            checkmate_1.9.4        cluster_2.1.0         
 [16] openxlsx_4.1.3         readr_1.3.1            magicfor_0.1.0        
 [19] ggfittext_0.8.1        lpSolve_5.6.13.3       prettyunits_1.0.2     
 [22] colorspace_1.4-1       mitools_2.4            pan_1.6               
 [25] haven_2.2.0            xfun_0.11              crayon_1.3.4          
 [28] jsonlite_1.6           lme4_1.1-21            zeallot_0.1.0         
 [31] zoo_1.8-6              iterators_1.0.12       glue_1.3.1            
 [34] survminer_0.4.6        gtable_0.3.0           SmartEDA_0.3.2        
 [37] DEoptimR_1.0-8         jomo_2.6-10            scales_1.1.0          
 [40] DBI_1.0.0              GGally_1.4.0           Rcpp_1.0.3            
 [43] htmlTable_1.13.2       xtable_1.8-4           progress_1.2.2        
 [46] foreign_0.8-72         km.ci_0.5-2            Formula_1.2-3         
 [49] survey_3.36            DT_0.10                describer_0.2.0       
 [52] htmlwidgets_1.5.1      sampling_2.8           httr_1.4.1            
 [55] RColorBrewer_1.1-2     acepack_1.4.1          ellipsis_0.3.0        
 [58] mice_3.6.0             DataExplorer_0.8.0     pkgconfig_2.0.3       
 [61] reshape_0.8.8          farver_2.0.1           nnet_7.3-12           
 [64] utf8_1.1.4             janitor_1.2.0          tidyselect_0.2.5      
 [67] labeling_0.3           rlang_0.4.1            later_1.0.0           
 [70] munsell_0.5.0          reactR_0.4.1           cellranger_1.1.0      
 [73] tools_3.6.0            cli_1.1.0              jmvcore_1.0.8         
 [76] generics_0.0.2         broom_0.5.2            evaluate_0.14         
 [79] stringr_1.4.0          fastmap_1.0.1          yaml_2.2.0            
 [82] knitr_1.26             zip_2.0.4              pander_0.6.3          
 [85] robustbase_0.93-5      survMisc_0.5.5         purrr_0.3.3           
 [88] mitml_0.3-7            visdat_0.5.3           nlme_3.1-142          
 [91] jmv_1.0.8              reactable_0.1.0        mime_0.7              
 [94] formatR_1.7            rstudioapi_0.10.0-9003 compiler_3.6.0        
 [97] curl_4.2               ggsignif_0.6.0         e1071_1.7-2           
[100] tibble_2.1.3           stringi_1.4.3          highr_0.8             
[103] parameters_0.2.5.1     forcats_0.4.0          lattice_0.20-38       
[106] whoami_1.3.0           Matrix_1.2-17          nloptr_1.2.1          
[109] ggsci_2.9              KMsurv_0.1-5           vctrs_0.2.0           
[112] pillar_1.4.2           lifecycle_0.1.0        networkD3_0.4         
[115] inspectdf_0.0.7        data.table_1.12.6      insight_0.7.0         
[118] httpuv_1.5.2           R6_2.4.1               latticeExtra_0.6-28   
[121] wakefield_0.3.4        promises_1.1.0         renv_0.8.3            
[124] gridExtra_2.3          rio_0.5.16             codetools_0.2-16      
[127] boot_1.3-23            MASS_7.3-51.4          assertthat_0.2.1      
[130] rprojroot_1.3-2        rjson_0.2.20           withr_2.1.2           
[133] explore_0.5.1          bayestestR_0.4.0.1     parallel_3.6.0        
[136] hms_0.5.2              rpart_4.1-15           grid_3.6.0            
[139] labelled_2.2.1         minqa_1.2.4            tidyr_1.0.0           
[142] class_7.3-15           rmarkdown_1.17         snakecase_0.11.0      
[145] ggpubr_0.2.4           shiny_1.4.0            lubridate_1.7.4       
[148] base64enc_0.1-3

6.1.2 Loaded packages

pacman::p_loaded(all = TRUE)

6.1.3 Notes

Last update on 2019-11-27 12:00:41

Serdar Balci, MD, Pathologist
drserdarbalci@gmail.com
https://rpubs.com/sbalci/CV

6.2 Code Appendix

Use following chunk options to include all codes below the report.

# installing necessary packages
if (!require("remotes")) install.packages("remotes")
if (!require("pacman")) install.packages("pacman")
if (!require("pak")) install.packages("pak")
if (!require("here")) install.packages("here")
knitr::opts_chunk$set(
    eval = TRUE,
    echo = TRUE,
    fig.path = here::here("figs/"),
    message = FALSE,
    warning = FALSE,
    error = FALSE,
    cache = FALSE,
    comment = NA,
    tidy = TRUE,
    fig.width = 6,
    fig.height = 4
)
# https://cran.r-project.org/web/packages/exploreR/vignettes/exploreR.html
# exploreR::reset()
source(file = here::here("R", "loadLibrary.R"))
source(file = here::here("R", "gc_fake_data.R"))
wakefield::table_heat(x = fakedata, palette = "Set1", flip = TRUE, print = TRUE)
library(readxl)
mydata <- readxl::read_excel(here::here("data", "mydata.xlsx"))
# View(mydata) # Use to view data after importing
# https://cran.r-project.org/web/packages/rio/vignettes/rio.html
# rio::install_formats()

x <- rio::import("mtcars.csv")
y <- rio::import("mtcars.rds")
z <- rio::import("mtcars.dta")

rio::import("mtcars_noext", format = "csv")

rio::export(mtcars, "mtcars.csv")
rio::export(mtcars, "mtcars.rds")
rio::export(mtcars, "mtcars.dta")

rio::export(list(mtcars = mtcars, iris = iris), "multi.xlsx")

# Dataframe report
mydata %>% 
  select(-contains("Date")) %>%
  report::report(.)
mydata %>% explore::describe_tbl()
dput(names(mydata))
keycolumns <-  
    mydata %>%  
    sapply(., FUN = dataMaid::isKey) %>%  
    as_tibble() %>%  
    select(  
        which(.[1, ] == TRUE)  
    ) %>%   
    names()  
keycolumns  
mydata %>% 
  select(-keycolumns) %>% 
inspectdf::inspect_types()
mydata %>% 
    select(-keycolumns,
           -contains("Date")) %>% 
  describer::describe() %>% 
  knitr::kable(format = "markdown")
mydata %>% 
    select(-keycolumns) %>% 
  inspectdf::inspect_types() %>% 
  inspectdf::show_plot()
# https://github.com/ropensci/visdat
# http://visdat.njtierney.com/articles/using_visdat.html
# https://cran.r-project.org/web/packages/visdat/index.html
# http://visdat.njtierney.com/

# visdat::vis_guess(mydata)

visdat::vis_dat(mydata)
mydata %>% explore::explore_tbl()
mydata %>% 
    select(-keycolumns) %>% 
    inspectdf::inspect_types() %>% 
    dplyr::filter(type == "character") %>% 
    dplyr::select(col_name) %>% 
    pull() %>% 
    unlist() -> characterVariables

characterVariables
mydata %>%
    dplyr::select(-keycolumns,
                  -contains("Date")
                  ) %>%
  describer::describe() %>% 
    janitor::clean_names() %>% 
    dplyr::filter(column_type == "factor") %>% 
    dplyr::select(column_name) %>% 
    dplyr::pull() -> categoricalVariables

categoricalVariables
mydata %>%
    dplyr::select(-keycolumns,
                  -contains("Date")) %>%
  describer::describe() %>% 
    janitor::clean_names() %>% 
    dplyr::filter(column_type == "numeric" | column_type == "double") %>% 
    dplyr::select(column_name) %>% 
    dplyr::pull() -> continiousVariables

continiousVariables
mydata %>% 
    select(-keycolumns) %>% 
inspectdf::inspect_types() %>% 
  dplyr::filter(type == "numeric") %>% 
  dplyr::select(col_name) %>% 
  pull() %>% 
  unlist() -> numericVariables

numericVariables
mydata %>% 
    select(-keycolumns) %>% 
inspectdf::inspect_types() %>% 
  dplyr::filter(type == "integer") %>% 
  dplyr::select(col_name) %>% 
  pull() %>% 
  unlist() -> integerVariables

integerVariables
mydata %>% 
    select(-keycolumns) %>% 
inspectdf::inspect_types() %>% 
  dplyr::filter(type == "list") %>% 
  dplyr::select(col_name) %>% 
  pull() %>% 
  unlist() -> listVariables
listVariables
is_date <- function(x) inherits(x, c("POSIXct", "POSIXt"))

dateVariables <- 
names(which(sapply(mydata, FUN = is_date) == TRUE))
dateVariables
View(mydata)
reactable::reactable(data = mydata, sortable = TRUE, resizable = TRUE, filterable = TRUE, searchable = TRUE, pagination = TRUE, paginationType = "numbers", showPageSizeOptions = TRUE, highlight = TRUE, striped = TRUE, outlined = TRUE, compact = TRUE, wrap = FALSE, showSortIcon = TRUE, showSortable = TRUE)
summarytools::view(summarytools::dfSummary(mydata %>% select(-keycolumns)))
if(!dir.exists(here::here("out"))) {dir.create(here::here("out"))}

summarytools::view(
  x = summarytools::dfSummary(
    mydata %>% 
      select(-keycolumns)
    ),
  file = here::here("out", "mydata_summary.html")
)
if(!dir.exists(here::here("out"))) {dir.create(here::here("out"))}

dataMaid::makeDataReport(data = mydata, 
                         file = here::here("out", "dataMaid_mydata.Rmd"),
                         replace = TRUE,
                         openResult = FALSE, 
                         render = FALSE,
                         quiet = TRUE
                         )
if(!dir.exists(here::here("out"))) {dir.create(here::here("out"))}

mydata %>% 
  select(
    -dateVariables
  ) %>% 
  explore::report(
    output_file = "mydata_report.html",
    output_dir = here::here("out") 
    )
glimpse(mydata %>% select(-keycolumns, -dateVariables))
mydata %>% explore::describe()
explore::explore(mydata)
mydata %>%
  explore::explore_all()
visdat::vis_expect(data = mydata,
                   expectation = ~.x == -1,
                   show_perc = TRUE)

visdat::vis_expect(mydata, ~.x >= 25)
visdat::vis_miss(airquality,
                 cluster = TRUE)
visdat::vis_miss(airquality,
         sort_miss = TRUE)
xray::anomalies(mydata)
xray::distributions(mydata)
DataExplorer::plot_str(mydata)
DataExplorer::plot_str(mydata, type = "r")
DataExplorer::introduce(mydata)
DataExplorer::plot_intro(mydata)
DataExplorer::plot_missing(mydata)
mydata2 <- DataExplorer::drop_columns(mydata, "TStage")
DataExplorer::plot_bar(mydata)
DataExplorer::plot_bar(mydata, with = "Death")
DataExplorer::plot_histogram(mydata)
if(!dir.exists(here::here("out"))) {dir.create(here::here("out"))}

# https://cran.r-project.org/web/packages/dataMaid/vignettes/extending_dataMaid.html
library("dataMaid")
dataMaid::makeDataReport(mydata,
  #add extra precheck function
  preChecks = c("isKey", "isSingular", "isSupported", "isID"),

  #Add the extra summaries - countZeros() for character, factor,
  #integer, labelled and numeric variables and meanSummary() for integer,
  #numeric and logical variables:
  summaries = setSummaries(
    character = defaultCharacterSummaries(add = "countZeros"),
    factor = defaultFactorSummaries(add = "countZeros"),
    labelled = defaultLabelledSummaries(add = "countZeros"),
    numeric = defaultNumericSummaries(add = c("countZeros", "meanSummary")),
    integer = defaultIntegerSummaries(add = c("countZeros", "meanSummary")),
    logical = defaultLogicalSummaries(add =  c("meanSummary"))
  ),

  #choose mosaicVisual() for categorical variables,
  #prettierHist() for all others:
  visuals = setVisuals(
    factor = "mosaicVisual",
    numeric = "prettierHist",
    integer = "prettierHist",
    Date = "prettierHist"
  ),

  #Add the new checkFunction, identifyColons, for character, factor and
  #labelled variables:
  checks = setChecks(
    character = defaultCharacterChecks(add = "identifyColons"),
    factor = defaultFactorChecks(add = "identifyColons"),
    labelled = defaultLabelledChecks(add = "identifyColons")
  ),

  #overwrite old versions of the report, render to html and don't
  #open the html file automatically:
  replace = TRUE,
  output = "html",
  open = FALSE,
  file = here::here("out/dataMaid_mydata.Rmd")
)
# https://cran.r-project.org/web/packages/summarytools/vignettes/Recommendations-rmarkdown.html
# https://github.com/dcomtois/summarytools
library(knitr)
opts_chunk$set(comment=NA,
               prompt=FALSE,
               cache=FALSE,
               echo=TRUE,
               results='asis' # add to individual summarytools chunks
               )
library(summarytools)
st_css()
st_options(bootstrap.css     = FALSE,       # Already part of the theme so no need for it
           plain.ascii       = FALSE,       # One of the essential settings
           style             = "rmarkdown", # Idem.
           dfSummary.silent  = TRUE,        # Suppresses messages about temporary files
           footnote          = NA,          # Keeping the results minimalistic
           subtitle.emphasis = FALSE)       # For the vignette theme, this gives
                                            # much better results. Your mileage may vary.
summarytools::freq(iris$Species, plain.ascii = FALSE, style = "rmarkdown")
summarytools::freq(iris$Species, report.nas = FALSE, headings = FALSE, cumul = TRUE, totals = TRUE)
summarytools::freq(tobacco$gender, style = 'rmarkdown')
summarytools::freq(tobacco[ ,c("gender", "age.gr", "smoker")])
print(freq(tobacco$gender), method = 'render')
view(dfSummary(iris))
dfSummary(tobacco, style = 'grid', graph.magnif = 0.75, tmp.img.dir = "/tmp")
dfSummary(tobacco, plain.ascii = FALSE, style = "grid",
          graph.magnif = 0.75, valid.col = FALSE, tmp.img.dir = "/tmp")
print(dfSummary(tobacco, graph.magnif = 0.75), method = 'render')
# https://github.com/rolkra/explore
# https://cran.r-project.org/web/packages/explore/vignettes/explore.html
# https://cran.r-project.org/web/packages/explore/vignettes/explore_mtcars.html


# library(dplyr)
# library(explore)

explore::explore(mydata)

# iris %>% report(output_file = "report.html", output_dir = here::here())


# iris$is_versicolor <- ifelse(iris$Species == "versicolor", 1, 0)
# iris %>%
#   report(output_file = "report.html",
#          output_dir = here::here(),
#          target = is_versicolor
# # , split = FALSE
# )
iris %>% explore::explore_tbl()

iris %>% explore::describe_tbl()

iris %>% explore::explore(Species)
iris %>% explore::explore(Sepal.Length)
iris %>% explore::explore(Sepal.Length, target = is_versicolor)
iris %>% explore::explore(Sepal.Length, target = is_versicolor, split = FALSE)
iris %>% explore::explore(Sepal.Length, target = Species)
iris %>% explore::explore(Sepal.Length, target = Petal.Length)


%>% %>%
  explore::explore_all()

iris %>%
  select(Sepal.Length, Sepal.Width) %>%
  explore::explore_all()

iris %>%
  select(Sepal.Length, Sepal.Width, is_versicolor) %>%
  explore::explore_all(target = is_versicolor)

iris %>%
  select(Sepal.Length, Sepal.Width, is_versicolor) %>%
  explore::explore_all(target = is_versicolor, split = FALSE)

iris %>%
  select(Sepal.Length, Sepal.Width, Species) %>%
  explore::explore_all(target = Species)

iris %>%
  select(Sepal.Length, Sepal.Width, Petal.Length) %>%
  explore::explore_all(target = Petal.Length)
iris %>%
  explore::explore_all()

knitr::opts_current(fig.height=explore::total_fig_height(iris, target = Species))

explore::total_fig_height(iris, target = Species)

iris %>% explore::explore_all(target = Species)
iris %>% explore::explore(Sepal.Length, min_val = 4.5, max_val = 7)
iris %>% explore::explore(Sepal.Length, auto_scale = FALSE)
mtcars %>% explore::describe()
# https://cran.r-project.org/web/packages/dlookr/vignettes/EDA.html

dlookr::describe(mydata
                 # ,
                 # cols = c(statistic)
                 )

# dlookr::describe(carseats, Sales, CompPrice, Income)
# dlookr::describe(carseats, Sales:Income)
# dlookr::describe(carseats, -(Sales:Income))
mydata %>%
  dlookr::describe() %>%
  dplyr::select(variable, skewness, mean, p25, p50, p75) %>%
  dplyr::filter(!is.na(skewness)) %>%
  arrange(desc(abs(skewness)))
# https://cran.r-project.org/web/packages/dlookr/vignettes/EDA.html

carseats %>%
  dlookr::eda_report(target = Sales,
                     output_format = "pdf",
             output_file = "EDA.pdf"
             )
carseats %>%
  dlookr::eda_report(target = Sales,
             output_format = "html",
             output_file = "EDA.html"
             )
# install.packages("ISLR")
library("ISLR")
# install.packages("SmartEDA")
library("SmartEDA")
## Load sample dataset from ISLR pacakge
Carseats <- ISLR::Carseats

## overview of the data;
SmartEDA::ExpData(data=Carseats,type=1)
## structure of the data
SmartEDA::ExpData(data=Carseats,type=2)


mydata %>%
  dplyr::select(-dplyr::contains("Date")) %>%
  report::report()
# cat(names(mydata), sep = " + \n")
library(arsenal)
tab1 <- arsenal::tableby(
  ~ Sex +
    Age +
    Race +
    PreinvasiveComponent +
    LVI +
    PNI +
    Death +
    Group +
    Grade +
    TStage +
    `Anti-X-intensity` +
    `Anti-Y-intensity` +
    LymphNodeMetastasis +
    Valid +
    Smoker +
    Grade_Level
  ,
  data = mydata
)
summary(tab1)
library(tableone)
mydata %>% 
  select(-keycolumns,
         -dateVariables
         ) %>% 
tableone::CreateTableOne(data = .)
# CreateTableOne(vars = myVars, data = mydata, factorVars = characterVariables)
# tab <- CreateTableOne(vars = myVars, data = pbc, factorVars = catVars)
# print(tab, showAllLevels = TRUE)
# ?print.TableOne
# summary(tab)
# print(tab, nonnormal = biomarkers)
# print(tab, nonnormal = biomarkers, exact = "stage", quote = TRUE, noSpaces = TRUE)
# tab3Mat <- print(tab3, nonnormal = biomarkers, exact = "stage", quote = FALSE, noSpaces = TRUE, printToggle = FALSE)
# write.csv(tab3Mat, file = "myTable.csv")
mydata %>% 
  select(
    continiousVariables,
    numericVariables,
    integerVariables
  ) %>% 
summarytools::descr(., style = 'rmarkdown')
print(summarytools::descr(mydata), method = 'render', table.classes = 'st-small')
mydata %>% 
  summarytools::descr(.,
                      stats = "common",
                      transpose = TRUE,
                      headings = FALSE
                      )
mydata %>% 
  summarytools::descr(stats = "common") %>%
  summarytools::tb()
mydata$Sex %>% 
  summarytools::freq(cumul = FALSE, report.nas = FALSE) %>%
  summarytools::tb()
mydata %>%
  explore::describe() %>%
  dplyr::filter(unique < 5)
mydata %>%
  explore::describe() %>%
  dplyr::filter(na > 0)
mydata %>% explore::describe()
source(here::here("R", "gc_desc_cat.R"))
tab <- 
  mydata %>% 
  select(
    -keycolumns
    ) %>% 
  tableone::CreateTableOne(data = .)
?print.CatTable
tab$CatTable
race_stats <- summarytools::freq(mydata$Race) 
print(race_stats,
      report.nas = FALSE,
      totals = FALSE,
      display.type = FALSE,
      Variable.label = "Race Group"
      )
mydata %>% explore::describe(PreinvasiveComponent)
## Frequency or custom tables for categorical variables
SmartEDA::ExpCTable(
  mydata,
  Target = NULL,
  margin = 1,
  clim = 10,
  nlim = 5,
  round = 2,
  bin = NULL,
  per = T
)
inspectdf::inspect_cat(mydata)

inspectdf::inspect_cat(mydata)$levels$Group
library(summarytools)

grouped_freqs <- stby(data = mydata$Smoker, 
                      INDICES = mydata$Sex, 
                      FUN = freq, cumul = FALSE, report.nas = FALSE)

grouped_freqs %>% tb(order = 2)
summarytools::stby(
  list(x = mydata$LVI, y = mydata$LymphNodeMetastasis), 
  mydata$PNI,
  summarytools::ctable
  )
with(mydata, 
     summarytools::stby(
       list(x = LVI, y = LymphNodeMetastasis), PNI,
       summarytools::ctable
       )
     )
SmartEDA::ExpCTable(
  mydata,
  Target = "Sex",
  margin = 1,
  clim = 10,
  nlim = NULL,
  round = 2,
  bin = 4,
  per = F
)
mydata %>% 
  select(characterVariables) %>% 
  select(PreinvasiveComponent,
         PNI,
         LVI
         ) %>% 
reactable::reactable(data = ., groupBy = c("PreinvasiveComponent", "PNI"), columns = list(
  LVI = reactable::colDef(aggregate = "count")
))
questionr:::icut()
source(here::here("R", "gc_desc_cont.R"))
tab <- tableone::CreateTableOne(data = mydata)
# ?print.ContTable
tab$ContTable
print(tab$ContTable, nonnormal = c("Anti-X-intensity"))
mydata %>% explore::describe(Age)
mydata %>% 
  select(continiousVariables) %>% 
SmartEDA::ExpNumStat(
  data = .,
  by = "A",
  gp = NULL,
  Qnt = seq(0, 1, 0.1),
  MesofShape = 2,
  Outlier = TRUE,
  round = 2
)
inspectdf::inspect_num(mydata, breaks = 10)
inspectdf::inspect_num(mydata)$hist$Age
inspectdf::inspect_num(mydata, breaks = 10) %>%
  inspectdf::show_plot()
grouped_descr <- summarytools::stby(data = mydata, 
                      INDICES = mydata$Sex, 
                      FUN = summarytools::descr, stats = "common")
# grouped_descr %>% summarytools::tb(order = 2)
grouped_descr %>% summarytools::tb()
mydata %>%
  group_by(US) %>% 
  dlookr::describe(Sales, Income) 

carseats %>%
  group_by(US, Urban) %>% 
  dlookr::describe(Sales, Income) 

categ <- dlookr::target_by(carseats, US)
cat_num <- dlookr::relate(categ, Sales)
cat_num
summary(cat_num)
plot(cat_num)
   summarytools::stby(data = mydata, 
                               INDICES = mydata$PreinvasiveComponent, 
                               FUN = summarytools::descr,
                      stats = c("mean", "sd", "min", "med", "max"), 
                               transpose = TRUE)
with(mydata, 
     summarytools::stby(Age, PreinvasiveComponent, summarytools::descr), 
                   stats = c("mean", "sd", "min", "med", "max"),
                   transpose = TRUE
                   )
mydata %>% 
  group_by(PreinvasiveComponent) %>% 
  summarytools::descr(stats = "fivenum")
## Summary statistics by – category
SmartEDA::ExpNumStat(
  mydata,
  by = "GA",
  gp = "PreinvasiveComponent",
  Qnt = seq(0, 1, 0.1),
  MesofShape = 2,
  Outlier = TRUE,
  round = 2
)
mydata %>% 
  janitor::tabyl(Sex) %>%
  janitor::adorn_pct_formatting(rounding = 'half up', digits = 1) %>%
  knitr::kable()
mydata %>% 
  janitor::tabyl(Race) %>%
  janitor::adorn_pct_formatting(rounding = 'half up', digits = 1) %>%
  knitr::kable()
mydata %>% 
  janitor::tabyl(PreinvasiveComponent) %>%
  janitor::adorn_pct_formatting(rounding = 'half up', digits = 1) %>%
  knitr::kable()
mydata %>% 
  janitor::tabyl(LVI) %>%
  janitor::adorn_pct_formatting(rounding = 'half up', digits = 1) %>%
  knitr::kable()
mydata %>% 
  janitor::tabyl(PNI) %>%
  janitor::adorn_pct_formatting(rounding = 'half up', digits = 1) %>%
  knitr::kable()
mydata %>% 
  janitor::tabyl(Group) %>%
  janitor::adorn_pct_formatting(rounding = 'half up', digits = 1) %>%
  knitr::kable()
mydata %>% 
  janitor::tabyl(Grade) %>%
  janitor::adorn_pct_formatting(rounding = 'half up', digits = 1) %>%
  knitr::kable()
mydata %>% 
  janitor::tabyl(TStage) %>%
  janitor::adorn_pct_formatting(rounding = 'half up', digits = 1) %>%
  knitr::kable()
mydata %>% 
  janitor::tabyl(LymphNodeMetastasis) %>%
  janitor::adorn_pct_formatting(rounding = 'half up', digits = 1) %>%
  knitr::kable()
mydata %>% 
  janitor::tabyl(Grade_Level) %>%
  janitor::adorn_pct_formatting(rounding = 'half up', digits = 1) %>%
  knitr::kable()
mydata %>% 
  janitor::tabyl(DeathTime) %>%
  janitor::adorn_pct_formatting(rounding = 'half up', digits = 1) %>%
  knitr::kable()
mydata %>% 
jmv::descriptives(
    data = .,
    vars = 'Age',
    hist = TRUE,
    dens = TRUE,
    box = TRUE,
    violin = TRUE,
    dot = TRUE,
    mode = TRUE,
    sd = TRUE,
    variance = TRUE,
    skew = TRUE,
    kurt = TRUE,
    quart = TRUE)
mydata %>% 
jmv::descriptives(
    data = .,
    vars = 'Anti-X-intensity',
    hist = TRUE,
    dens = TRUE,
    box = TRUE,
    violin = TRUE,
    dot = TRUE,
    mode = TRUE,
    sd = TRUE,
    variance = TRUE,
    skew = TRUE,
    kurt = TRUE,
    quart = TRUE)
mydata %>% 
jmv::descriptives(
    data = .,
    vars = 'Anti-Y-intensity',
    hist = TRUE,
    dens = TRUE,
    box = TRUE,
    violin = TRUE,
    dot = TRUE,
    mode = TRUE,
    sd = TRUE,
    variance = TRUE,
    skew = TRUE,
    kurt = TRUE,
    quart = TRUE)
mydata$int <- lubridate::interval(
  lubridate::ymd(mydata$SurgeryDate),
  lubridate::ymd(mydata$LastFollowUpDate)
  )
mydata$OverallTime <- lubridate::time_length(mydata$int, "month")
mydata$OverallTime <- round(mydata$OverallTime, digits = 1)
mydata$OverallTime <- mydata$genel_sagkalim
## Recoding mydata$Death into mydata$Outcome
mydata$Outcome <- forcats::fct_recode(as.character(mydata$Death),
               "1" = "TRUE",
               "0" = "FALSE")
mydata$Outcome <- as.numeric(as.character(mydata$Outcome))
table(mydata$Death, mydata$Outcome)
library(survival)
# data(lung)
# km <- with(lung, Surv(time, status))
km <- with(mydata, Surv(OverallTime, Outcome))
head(km,80)
plot(km)
# Drawing Survival Curves Using ggplot2
# https://rpkgs.datanovia.com/survminer/reference/ggsurvplot.html
dependentKM <- "Surv(OverallTime, Outcome)"
explanatoryKM <- "LVI"

mydata %>%
  finalfit::surv_plot(.data = .,
                      dependent = dependentKM,
                      explanatory = explanatoryKM,
                      xlab='Time (months)',
                      pval=TRUE,
                      legend = 'none',
                      break.time.by = 12,
                      xlim = c(0,60)
                      # legend.labs = c('a','b')
                      )
# Drawing Survival Curves Using ggplot2
# https://rpkgs.datanovia.com/survminer/reference/ggsurvplot.html

mydata %>%
  finalfit::surv_plot(.data = .,
                      dependent = "Surv(OverallTime, Outcome)",
                      explanatory = "LVI",
                      xlab='Time (months)',
                      pval=TRUE,
                      legend = 'none',
                      break.time.by = 12,
                      xlim = c(0,60)
                      # legend.labs = c('a','b')
                      )
library(finalfit)
library(survival)
explanatoryUni <- "LVI"
dependentUni <- "Surv(OverallTime, Outcome)"

mydata %>%
finalfit::finalfit(dependentUni, explanatoryUni) -> tUni

knitr::kable(tUni, row.names=FALSE, align=c('l', 'l', 'r', 'r', 'r', 'r'))
tUni_df <- tibble::as_tibble(tUni, .name_repair = "minimal") %>% 
  janitor::clean_names() 

tUni_df_descr <- paste0("When ",
                        tUni_df$dependent_surv_overall_time_outcome[1],
                        " is ",
                        tUni_df$x[2],
                        ", there is ",
                        tUni_df$hr_univariable[2],
                        " times risk than ",
                        "when ",
                        tUni_df$dependent_surv_overall_time_outcome[1],
                        " is ",
                        tUni_df$x[1],
                        "."
                        )
km_fit <- survfit(Surv(OverallTime, Outcome) ~ LVI, data = mydata)
km_fit
plot(km_fit)
# summary(km_fit)
km_fit_median_df <- summary(km_fit)
km_fit_median_df <- as.data.frame(km_fit_median_df$table) %>% 
  janitor::clean_names() %>% 
  tibble::rownames_to_column()
km_fit
broom::tidy(km_fit)
km_fit_median_df %>% 
  dplyr::mutate(
    description = 
      glue::glue(
      "When {rowname}, median survival is {median} [{x0_95lcl} - {x0_95ucl}, 95% CI] months."
    )
  ) %>% 
  dplyr::select(description) %>% 
  pull() -> km_fit_median_definition
summary(km_fit, times = c(12,36,60))
km_fit_summary <- summary(km_fit, times = c(12,36,60))

km_fit_df <- as.data.frame(km_fit_summary[c("strata", "time", "n.risk", "n.event", "surv", "std.err", "lower", "upper")])
km_fit_df %>% 
  dplyr::mutate(
    description = 
      glue::glue(
      "When {strata}, {time} month survival is {scales::percent(surv)} [{scales::percent(lower)}-{scales::percent(upper)}, 95% CI]."
    )
  ) %>% 
  dplyr::select(description) %>% 
  pull() -> km_fit_definition
library(survival)
surv_fit <- survival::survfit(Surv(time, status) ~ ph.ecog, data=lung)
insight::is_model_supported(surv_fit)
insight::find_formula(surv_fit)
report::report_participants(mydata)
dependentKM <-  "Surv(OverallTime, Outcome2)"

explanatoryKM <- c("explanatory1",
               "explanatory2"
               )
source(here::here("R", "gc_survival.R"))
mydependent <-  "Surv(time, status)"
explanatory <- "Organ"

mysurvival <- function(mydata, mydependent, explanatory) {
    {{mydata}} %>%
        finalfit::surv_plot(dependent = {{mydependent}},
                            explanatory = {{explanatory}},
                            xlab='Time (months)',
                            pval=TRUE,
                            legend = 'none',
                            break.time.by = 12,
                            xlim = c(0,60)
        )
}


# library(tidyverse)
mysurvival(mydata = whippleables, mydependent = mydependent, explanatory = explanatory)

explanatory <- c("Organ", "LVI")

deneme <- purrr::map(explanatory, mysurvival, mydata = whippleables, mydependent = mydependent)

dependentKM <- "Surv(OverallTime, Outcome)"
explanatoryKM <- "TStage"

mydata %>%
  finalfit::surv_plot(.data = .,
                      dependent = dependentKM,
                      explanatory = explanatoryKM,
                      xlab='Time (months)',
                      pval=TRUE,
                      legend = 'none',
                      break.time.by = 12,
                      xlim = c(0,60)
                      # legend.labs = c('a','b')
                      )
survminer::pairwise_survdiff(
  formula = Surv(OverallTime, Outcome) ~ TStage, 
                             data = mydata,
                             p.adjust.method = "BH"
  )
library(finalfit)
library(survival)
explanatoryMultivariate <- explanatoryKM
dependentMultivariate <- dependentKM

mydata %>%
  finalfit(dependentMultivariate, explanatoryMultivariate) -> tMultivariate

knitr::kable(tMultivariate, row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))
projectName <- list.files(path = here::here(), pattern = "Rproj")
projectName <- gsub(pattern = ".Rproj", replacement = "", x = projectName)

analysisDate <- as.character(Sys.Date())

imageName <- paste0(projectName, analysisDate, ".RData")

save.image(file = here::here("data", imageName))

rdsName <- paste0(projectName, analysisDate, ".rds")

readr::write_rds(x = mydata, path = here::here("data", rdsName))

saveRDS(object = mydata, file = here::here("data", rdsName))

excelName <- paste0(projectName, analysisDate, ".xlsx")

rio::export(
  x = mydata,
  file = here::here("data", excelName),
  format = "xlsx"
)

# writexl::write_xlsx(mydata, here::here("data", excelName))

print(glue::glue(
    "saved data after analysis to ",
    rownames(file.info(here::here("data", excelName))),
    " : ",
    as.character(
        file.info(here::here("data", excelName))$ctime
    )
    )
)
# pacman::p_load(here, lubridate, glue)
# here::here("data", glue("{today()}_trends.csv"))
# mydata %>% select(
#     -c(
#         rapor_yil,
#         rapor_no,
#         protokol_no,
#         istek_tarihi,
#         nux_yada_met_varsa_tarihi,
#         son_hastane_vizit_tarihi,
#         Outcome
#     )
# ) -> finalSummary
# 
# summarytools::view(summarytools::dfSummary(x = finalSummary
#                                            , style = "markdown"))
citation()
report::cite_packages(session = sessionInfo())
report::show_packages(session = sessionInfo()) %>% 
    kableExtra::kable()
citation("tidyverse")
citation("readxl")
citation("janitor")
citation("report")
citation("finalfit")
# citation("ggstatsplot")
if(!dir.exists(here::here("bib"))) {dir.create(here::here("bib"))}

knitr::write_bib(x = c(.packages(), "knitr", "shiny"),
                 file = here::here("bib", "packages.bib")
)
sessionInfo()
pacman::p_loaded(all = TRUE)

References

Knijn, N., F. Simmer, and I. D. Nagtegaal. 2015. “Recommendations for Reporting Histopathology Studies: A Proposal.” Virchows Archiv 466 (6): 611–15. https://doi.org/10.1007/s00428-015-1762-3.

Schmidt, Robert L., Deborah J. Chute, Jorie M. Colbert-Getz, Adolfo Firpo-Betancourt, Daniel S. James, Julie K. Karp, Douglas C. Miller, et al. 2017. “Statistical Literacy Among Academic Pathologists: A Survey Study to Gauge Knowledge of Frequently Used Statistical Tests Among Trainees and Faculty.” Archives of Pathology & Laboratory Medicine 141 (2): 279–87. https://doi.org/10.5858/arpa.2016-0200-OA.

From Table 1: Proposed items for reporting histopathology studies. Recommendations for reporting histopathology studies: a proposal Virchows Arch (2015) 466:611–615 DOI 10.1007/s00428-015-1762-3↩︎
From Table 1: Proposed items for reporting histopathology studies. Recommendations for reporting histopathology studies: a proposal Virchows Arch (2015) 466:611–615 DOI 10.1007/s00428-015-1762-3↩︎
See childRmd/_01header.Rmd file for other general settings↩︎
Change echo = FALSE to hide codes after knitting.↩︎
See childRmd/_02fakeData.Rmd file for other codes↩︎
Synthea The validity of synthetic clinical data: a validation study of a leading synthetic data generator (Synthea) using clinical quality measures. BMC Med Inform Decis Mak 19, 44 (2019) doi:10.1186/s12911-019-0793-0 ↩︎
https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-019-0793-0 ↩︎
Synthetic Patient Generation ↩︎
Basic Setup and Running ↩︎
intelligent patient data generator (iPDG)↩︎
https://medium.com/free-code-camp/how-our-test-data-generator-makes-fake-data-look-real-ace01c5bde4a ↩︎
https://forums.librehealth.io/t/demo-data-generation/203 ↩︎
https://mihin.org/services/patient-generator/↩︎
lung, cancer, breast datası ile birleştir↩︎
See childRmd/_03importData.Rmd file for other codes↩︎
See childRmd/_04briefSummary.Rmd file for other codes↩︎
https://www.hhs.gov/hipaa/index.html ↩︎
Kişisel verilerin kaydedilmesi ve kişisel verileri hukuka aykırı olarak verme veya ele geçirme Türk Ceza Kanunu’nun 135. ve 136. maddesi kapsamında bizim hukuk sistemimizde suç olarak tanımlanmıştır. Kişisel verilerin kaydedilmesi suçunun cezası 1 ila 3 yıl hapis cezasıdır. Suçun nitelikli hali ise, kamu görevlisi tarafından görevin verdiği yetkinin kötüye kullanılarak veya belirli bir meslek veya sanatın sağladığı kolaylıktan yararlanılarak işlenmesidir ki bu durumda suçun cezası 1.5 ile 4.5 yıl hapis cezası olacaktır.↩︎
See childRmd/_06variableTypes.Rmd file for other codes↩︎
See childRmd/_07overView.Rmd file for other codes↩︎
Statistical Literacy Among Academic Pathologists: A Survey Study to Gauge Knowledge of Frequently Used Statistical Tests Among Trainees and Faculty. Archives of Pathology & Laboratory Medicine: February 2017, Vol. 141, No. 2, pp. 279-287. https://doi.org/10.5858/arpa.2016-0200-OA ↩︎
From Table 1: Proposed items for reporting histopathology studies. Recommendations for reporting histopathology studies: a proposal Virchows Arch (2015) 466:611–615 DOI 10.1007/s00428-015-1762-3↩︎
See childRmd/_11descriptives.Rmd file for other codes↩︎
See childRmd/_18survival.Rmd file for other codes, and childRmd/_19shinySurvival.Rmd for shiny application↩︎
JAMA retraction after miscoding – new Finalfit function to check recoding ↩︎
See childRmd/_23footer.Rmd file for other codes↩︎
Smith AM, Katz DS, Niemeyer KE, FORCE11 Software Citation Working Group. (2016) Software Citation Principles. PeerJ Computer Science 2:e86. DOI: 10.7717/peerj-cs.86 https://www.force11.org/software-citation-principles ↩︎

A work by Serdar Balci

drserdarbalci@gmail.com

Histopathology Research Template

Serdar Balci, MD, Pathologist

2019-11-27